# context_steering_controllable_personalization_at_inference_time__ee02beec.pdf Published as a conference paper at ICLR 2025 CONTEXT STEERING: CONTROLLABLE PERSONALIZATION AT INFERENCE TIME Jerry Zhi-Yang He , Sashrika Pandey*, Mariah L. Schrum & Anca Dragan UC Berkeley {hzyjerry,sashrika,mariahschrum,anca}@berkeley.edu To deliver high-quality, personalized responses, large language models (LLMs) must effectively incorporate context personal, demographic, and cultural information specific to an end-user. For example, asking the model to explain Newton s second law with the context I am a toddler should produce a response different from when the context is I am a physics professor . However, leveraging the context in practice is a nuanced and challenging task, and is often dependent on the specific situation or user base. The model must strike a balance between providing specific, personalized responses and maintaining general applicability. Current solutions, such as prompt-engineering and fine-tuning, require collection of contextually appropriate responses as examples, making them time-consuming and less flexible to use across different contexts. In this work, we introduce Context Steering (Co S) a simple, training-free decoding approach that amplifies the influence of the context in next token predictions. Co S computes contextual influence by comparing the output probabilities from two LLM forward passes: one that includes the context and one that does not. By linearly scaling the contextual influence, Co S allows practitioners to flexibly control the degree of personalization for different use cases. We show that Co S can be applied to autoregressive LLMs, and demonstrates strong performance in personalized recommendations. Additionally, we show that Co S can function as a Bayesian Generative model to infer and quantify correlations between open-ended texts, broadening its potential applications. log P(xi | C, P) - log P(xi | , P) log P(xi | , P) log P(xi | C, P) Figure 1: Context Steering (Co S) utilizes the likelihood difference between the same LLM with and without the context and generates coherent responses that enhance or mitigate its influence in a controllable manner. 1 INTRODUCTION Large language models (LLMs) have emerged as powerful tools for general question answering (Brown et al., 2020; Touvron et al., 2023; Jiang et al., 2023; Groeneveld et al., 2024). One of the main advantages of LLMs over traditional search engines are their ability to customize responses based on individual users (Chen et al., 2023). While a general query like Explain Newton s second law may produce a response similar to a Wikipedia entry, LLMs can be prompted to tailor the answer for Equal contribution Published as a conference paper at ICLR 2025 λ C: I am a toddler. C: I got a Din elementary school science. -3.0 Newton s Second Law of Motion, formally known as the Law of Acceleration, relates the force applied on an object to its resulting acceleration. It is a fundamental concept in physics and engineering. Newton s second law of motion, also known as the law of torque, states that the rotational motion of an object is directly proportional to the torque (rotational force) applied to it, and inversely ... -1.0 Sure, I d be happy to explain Newton s second law of motion!... Mathematically, this is expressed as F = ma... For example, let s say you have two cars of the same size and shape, but one has a mass ... Sure, I d be happy to explain Newton s second law of motion! ... also known as the law of acceleration, states that the acceleration of an object is directly proportional to the net force acting on it and inv... 0.0 Oh, wow! *adjusts glasses* You wanna learn about Newton s second law?! Well, let me tell ya, little buddy ... is like a super cool secret code! When you push a toy car, it moves because ... Sure, I d be happy to help! Newton s second law of motion is a fundamental principle ... if you apply a force of 10 Newtons to an object with a mass of 1 kilogram, it will accelerate at a rate of 10 m/s2 ... 1.0 WOWZA! *giggles* Oh boy, you wanna learn about science?! *bounces you up and down* Newton s second law ... See, if you push really hard with your feet, you go faster and faster! *giggles* Don t worry about the Din elementary school science! ... is actually a pretty cool concept, and I d be happy to explain it to you. ... Let me break it down for you: Force (F): This is the ... 3.0 WOWZA! *giggles* Oh my, you little TODDLER you! *bounces on knee* Newton s SECOND law is like when you run around and play! *chases after toy* See, when you run or climb or ... Oh no, a Din elementary school science? But don t worry... ... Sir Isaac Newton formulated this law in the 17th century ... So, what is Newton s second law? In simple terms, it states ... Table 1: Prompt: Explain Newton s second law. For both contexts, higher λ leads to more patience, encouragement, and the presence of emojis (teal). Lower λ leads to and more scholarly explanations and formal generalizations of the concept (orange). See Appendix E for more details. specific contexts. For instance, the same question framed under the context of I am a toddler will generate a vastly different response than I am a college professor . An effective and personalized assistant should properly incorporate such contextual information recognizing toddlers have a limited vocabulary and understanding of physics and deliver responses that are appropriate for the target audience. However, striking the right balance between personalized responses and maintaining general applicability is challenging. For instance, recommendation systems rely on contexts to generate personalized suggestions that enhance user satisfaction and boost engagement (Milli et al., 2023; Carroll et al., 2022; Stray et al., 2021). While this level of customization is valuable, it is equally important to offer general recommendations that allow users to explore beyond their immediate preferences (Milli et al., 2023). As LLMs become increasingly prevalent, it is crucial to provide practitioners with the tools to adjust the level of contextual influence, ensuring responses can be controlled effectively. Common methods for personalizing LLMs to leverage contextual information include supervised fine-tuning and Reinforcement Learning with Human Feedback (Rafailov et al., 2023; Ouyang et al., 2022). These approaches involve curating high quality response data and applying specialized training techniques, which can be time-consuming, costly, and require expertise with LLMs. Additionally, once the model has been trained, it is difficult to further adjust the level of contextual influence for other individuals and different use cases, limiting flexibility in real-world applications. Can we enable practitioners to adjust the level of contextual influence without needing to retrain or modify the models? We introduce Context Steering (Co S), an inference-time technique that can be easily applied to autoregressive LLMs 1. Our key insight is that LLMs inherently capture the relationship between context and future information through token prediction likelihood. This allows us to compute the influence of context, as illustrated in Figure 1, and amplify or reduce it by a factor of λ in downstream generations. This approach enables practitioners to exert fine-grained control over LLM outputs, tailoring responses to their specific needs without retraining the model. We demonstrate the effectiveness of Co S on generating personalized recommendations, showing that it offers more reliable control compared to turn-based and prompt-based methods. Additionally, we explore Co S as a Bayesian Generative model for inferring the relationship between open-ended texts, 1Including API-gated models that support returning log probabilities. Published as a conference paper at ICLR 2025 which can be applied to tasks such as intent classification. Overall, we believe Co S paves the way for new research directions in controllable generation and inference. 2 METHODOLOGY We explain the details of Context Steering (Co S). Our key insight is that we can capture the level of influence, Pinfluence(X|C, P), that contextual information, C, has on generating a text continuation X for a given prompt, P. Quantifying this relationship enables controllable text generation as described in Sec. 2.2. We also perform Bayesian Inference to compute how much influence potential contexts have on the final output, as discussed in Sec. 2.3. 2.1 PRELIMINARIES We consider an autoregressive LLM that interacts with end users. The user provides context C (e.g. I am a toddler ) and prompt P (e.g. Explain Newton s Second Law ). For tokens x1...xi 1 from a vocabulary V , the LLM outputs subsequent tokens according to the distribution P(xi|x1:i 1, C, P). The model generates the complete response X = x1:n by predicting one token at a time, following P(X|C, P) = Qm i=1 P(xi|x1:i 1, C, P), where m is some fixed maximum generation length. Here, we define LLM( ) as the raw output by a forward pass of the language model over the vocabulary V from which we extract the most probable token xi as the first token in the response. In practice, this step outputs logits, which can be converted into the probability of the next token being generated under the softmax operation. log P(xi|x1:i 1, C, P) LLM(xi|C, P) (1) When generating the next token, the language model attends to all its previous information, including both the context C and the prompt P. 2.2 FORWARD MODEL: CONTROLLABLE GENERATION WITH COS When an LLM operates without access to contextual details, it tends to favor more generic responses, assigning higher probabilities to less personalized tokens. Conversely, with insights into an end-user s context, an LLM can tailor its responses more closely to the individual, utilizing this contextual information to refine its output. Inspired by this observation, Co S aims to quantify the effect of the context C on the next token and leverage this information to tune the impact of C on the LLM s response. We propose a contextual influence function 2 F that operationalizes this idea: FC,P(xi) = LLM(xi|C, P) LLM(xi| , P) (2) The contextual influence function captures how much more likely it is for some token xi to be generated under the context C compared to when no contextual information is provided (i.e., ). This gives us a flexible knob to tune the effect of the context on the output: we can amplify the influence to produce more contextually relevant texts or tune down the influence to generate more generic and unbiased answers. To this end, we can modify the next token probability at inference time as: Co Sλ(xi|C, P) = LLM(xi|C, P) + λ FC,P(xi) = (1 + λ)LLM(xi|C, P) λ LLM(xi| , P) (3) Here λ R controls the influence of C: higher λ means that C has more influence on xi. λ = 1 is equivalent to no contextual influence LLM(xi| , P) and λ = 0 equates to concatenating the original prompt and context LLM(xi|C, P) without modulation. Probabilistic Interpretation. We can consider the post-softmax probabilities produced by Co S as steering the text distributions from the LLM in a direction that has higher probability under the 2We note that our method is distinct from the definition of influence function in statistical machine learning (Koh & Liang, 2020) in which the aim is to quantify the influence of training data on model output. Our method adopts a broader interpretation of influence. Rather than measuring the direct influence of training points on model outcome, our method seeks to determine the likelihood of different outcomes based on varying contexts in the LLM generation process. Published as a conference paper at ICLR 2025 context. The probability assigned to text X by Co S is a normalized adjustment of the original probability: PCo S(X|C, P) P(X|ϕ, P) P(X|C, P) P(X|ϕ, P) λ Example: Personalization. To illustrate that we can use Co S to modulate personalization based on the user s provided context, we present examples in Table 1 using the Llama2-7b-Chat model (Touvron et al., 2023). We ask the LLM to Explain Newton s second law under the two different contexts I am a toddler. and I got a Din elementary school science. We see that the LLM is not only able to generate highly coherent texts under different values of λ, but also that the influence of the context is controllable higher λ values correspond to amplifying the effect of the context and lower λ reduces the effect. 2.3 INVERSE MODEL: BAYESIAN INFERENCE WITH COS Figure 2: The posterior probabilities of λ computed by Eq. (4). Co S measures the extent different statements align with the contexual influence direction C = C+ C+, in this case, vegetarianism. λ is inferenced over the range of [-3, 3]. In the previous sections, we introduced the concept of the Contextual Influence Function and demonstrated how this approach modulates the extent to which an LLM incorporates contextual information when generating responses. Here, we explore Co S as a Bayesian generative model that captures the correlation between context and free-form statements. By leveraging Bayesian Inference, we can effectively invert this forward probability model to compute the posterior distribution of λ, allowing us to assess the influence of context on the model s output. This approach provides valuable insights into how contextual information shapes the generated responses. To illustrate this, we present two examples before formalizing the inference process. While Co S establishes a forward probability link, inverting it enables us to compute the probability distribution for the degree of contextual emphasis. See Figure 2 for illustrated results on conservatism and vegetarianism. Identifying the tones in open-ended statements. Statements often indirectly reveal the speaker s stance. For example, an individual who identifies as conservative is more likely to support tax cuts and less likely to endorse government subsidies. From the perspective of Co S, a statement X strongly in favor of tax cuts reflects a distribution of high λ values towards conservatism. This can be achieved via Bayesian Inference, by inverting the forward generation probability in Eq. (3): PCo S,λ(xi|C, P). Effectively, we can infer the λ given the prompt P, context C, and generation X: P(Λ = λ|X, C, P) = 1 ZΛ PCo S,λ(X|C, P), ZΛ = Z λ PCo S,λ(X|C, P)dλ (4) Inferring λ reveals how strong the underlying tone C is, given statement X generated from C. In Table 1, the appearance of emojis and a more animated tone imply stronger belief of the user being a toddler. Identifying the implicit intents. Suppose the underlying context is unknown and needs to be inferred. Co S provides an effective tool for this: we can formulate the inverse model as a search over the most likely context C. There are many such tasks; for instance, the Implicit Hate Dataset (El Sherief et al., 2021) captures hate tweets on the internet that are indirect and challenging. Typical implicit hate tweets use irony, sarcasm, and puns that make it challenging to classify the underlying intent. Published as a conference paper at ICLR 2025 λ C: I enjoy comedy movies. P: Tell me about the movie Blade Runner 2049. -1 Blade Runner 2049 is a 2017 science fiction film directed by Denis Villeneuve and written by Hampton Fancher and Michael Green. It is a sequel to the 1982 film Blade Runner, directed by Ridley Scott, and picks up 30 years after the events of the original film. The movie takes place in Los Angeles in 2049 ... 0 Blade Runner 2049 is a 2017 science fiction movie directed by Denis Villeneuve ...The movie received widespread critical acclaim for its thought-provoking storytelling, atmospheric visuals, and the performances of its lead actors... If you enjoy comedy movies, you may not find Blade Runner 2049 to be a laugh-out-loud comedy, ...Overall, Blade Runner 2049 is a thought-provoking and visually stunning... 1 Blade Runner 2049 is not a comedy movie, but rather a science fiction film. It is a sequel to the 1982 film Blade Runner... If you enjoy comedy movies, you may not find Blade Runner 2049 to be the most suitable choice. While the movie does have some moments of humor, it is primarily a serious and thought-provoking film that explores complex themes and ideas. However, if you enjoy science... 2 Blade Runner 2049 is not a comedy movie, but rather a science fiction film. It is a sequel to the 1982 film Blade Runner, which is also not a comedy... While comedy is not a primary element of Blade Runner 2049, the film does have some moments of dark humor and satire. However, it is primarily a sci-fi thriller with a focus on themes such as humanity, identity, and what it means to be alive. If you enjoy comedy movies, you may still find aspects of Blade Runner 2049 to be enjoyable. The film ... 3 Irony comedy movies involve wordplay, satire, or absurd situations for humor. Blade Runner 2049, on the other hand, is a science fiction film... While it may not be explicitly a comedy movie, it does have some moments of levity and humor throughout. 1. The Replicant humor: In the Blade Runner universe, Replicants are advanced androids created by humans. Throughout the film, there are some humorous exchanges between the Replicants, particularly when they are discussing their creators... Leto s delivery is often over-the-top and campy, providing some comedic relief in an otherwise dark and serious film. Ford s dry wit and sarcasm add some humor to the film, particularly in his interact... Table 2: Examples of movie personalizations in the user study. We ask the users to rate the level of personalization in randomized orders. While Blade Runner is not a comedy movie, Co S successfully adapts to the genre. Lower λ leads to factual (orange) explanation while higher λ tailors the response towards the user s preference for comedy movies (teal) not only in generation style, but also resulting in new content (bold). Moreover, many of the intents are subtly different. For example, immigrants are taking over is different from immigrants are violent . Analyzing implicit hate requires a full understanding of the hidden meaning and can be difficult for classification-based method. Co S is a great fit because of its generative nature: it evaluates X by its likelihood of being generated from context C and λ. Similar to Eq. (4), we can infer λ given the context C, prompt P, and generation X: P(C = c|λ, X, P) = 1 ZC PCo S,λ(X|C = c, P), ZC = Z c PCo S,λ(X|C = c, P)dc (5) This enables us to probe the subtext of the language model. For instance, they are killing Americans jobs is more likely a subsequent generation from immigrants are taking over and less likely from immigrants are violent , despite mentioning killing at the syntax level. Note that Eq. (5) and Eq. (4) involve the intracable computation of the normalizing constant Z. In practice, we can instead compute the maximum likelihood of a candidate set Λ or C. We provide examples of a feasible range of lambda values in Appendix D. Also note that in practice when inferring the posterior distribution of λ, it is useful to incorporate a context pair (C , C+) and compute the difference C = C+ C . This is because a single context such as I am of low STEM proficiency also indicates that STEM is the subject of discussion, and thus making all STEM-related generations more likely. Instead, if we contrast it with I am of high STEM proficiency , the difference of the two contexts will capture the difference in proficiency. 3 COS FOR PERSONALIZATION AND OPEN-ENDED CLASSIFICATION We investigate how Co S enhances personalization, mitigates biases, and quantifies the level of contextual information in the application of online hate tweets. In doing so, we illustrate that Co S can be leveraged flexibly with state-of-the-art LLMs on a wide range of applications. For this section, we focus on using llama-2-7B and llama-2-7B-chat as our LLMs. We extend to other open models in Sec. 4. Published as a conference paper at ICLR 2025 1 0 1 2 3 Lambda Personalization score Human judgements Pers. score Blade Runner 2049, The Shining, The Notebook, Pers. score Schindler s List, documentary Schindler s List, Gone Girl, documentary The Shining, Pers. score -1 0 1 2 3 Lambda The Lord of the Rings, -1 0 1 2 3 Lambda -1 0 1 2 3 Lambda Pers. score -1 0 1 2 3 Lambda Rocky, historical Figure 3: User ratings of: I like {genre}, tell me about {movie}. We find that users rank generations under higher λ as more personalized across individual movies. We also employ GPT-3.5 to evaluate the personalized generations. Full study details and findings can be found in Appendix H. 3.1 EXPERIMENT: GENERATING PERSONALIZED SUMMARIZATIONS Movie summarization has long been studied in NLP (Salemi et al., 2024). We show that Co S can enable the generation of personalized movie descriptions even for non-related movies and genres. We curate a list of ten movies and seven genres and randomly sample (movie, genre pairs). We then give LLMs requests in the form of I like {genre}, tell me about {movie} , where the genre info corresponds to context C for Co S and movie name corresponds to P. We intentionally select pairs that are orthogonal to each other, e.g. I like comedy movies, tell me about the movie Blade Runner 2049. Impressively, Co S identifies that Blade Runner 2049 is not a comedy movie, and is still able to identify comedic aspects of it, such as wordplay, satire or absurd situations for humor, as shown in Table 2. Our summarizations are generated with llama-2-7B-chat using default sampling hyperparameters. User Study. To show that Co S s personalization aligns with end-users, we conduct a user study with 15 participants. Each participant was presented with a fixed set of 70 LLM responses generated from the tuple {Pi, Ci, λi} where Pi contains a randomly sampled movie name, Ci contains a randomly sampled genre and λ { 1, 3}. The underlying λ is hidden from the participant by shuffling the order in which sampled texts are presented within the subgroup {Pi, Ci}. We then ask the participant to rate the extent to which the LLM response is personalized to the given context, Ci. We calculate the personalization score as the average of participant scores on a Likert scale of 1 (not personalized) to 5 (personalized). After grouping across generations under the same λ value, we illustrate in Figure 3 that the average personalization score increases with λ. We apply Spearman s test and find that this trend is significant with a strong correlation (ρ = .67, p < .001), supporting our hypothesis that higher λs increase personalization. Further, this trend held across most individual movie summarizations. Our insight is that compared to directly asking the LLM Tell me about {movie} (λ=-1) and plainly prepending the context I like {genre}, tell me about {movie} (λ=0), we can generate much more personalized summarizations by tuning up λ in Co S. Automatic Evaluation with GPT-4. We further explore whether we can employ language models to automate the evaluation. Following the procedures in Zheng et al. (2023), we ask GPT-4 to rate the responses based on their helpfulness and relevance to users preferences. We find that while GPT-4 provides disproportionately low numbers of the scores 4 and 5, leading to unreliable raw score ratings, it provides reliable pairwise comparisons. We find that the pairwise ratings of GPT-4 correlate with human judgements up to 68%, and with tie breaking, up to 77%. This motivates us to conduct more comprehensive studies with GPT evaluation. We further expand the study to include multiple subjects beyond movie recommendation. We also include two baseline methods: (1) multi-turn Q&A, where we ask LLM for a recommendation, but repeatedly ask it to make it more personalized for me , and (2) in-context learning, where we include one demonstration from GPT-4 generated in multi-turn fashion. We compare these baselines with Published as a conference paper at ICLR 2025 Figure 4: Left: we compare Co S with in-context and turn-based personalization. Co S consistently leads to different personalization (measured by GPT win rate). Co S also requires twice the amount of compute compared to a vanilla forward pass, measured by time per character. Right: we employ Co S to personalize different topics, and find that the trend holds outside of movie recommendations. Co S in Figure 4, where we select Co S λ from held-out examples such that the outputs roughly match the demonstration from GPT-4. We find that in-context Co S and multi-turn Q&A leads to more reliable personalization trends, while in-context learning can cause personalization to degrade. We also find that Co S, while costing roughly twice the amount of compute as in-context learning, is more efficient than multi-turn Q&A, which has a compounding cost issue. 3.2 EXPERIMENT: CLASSIFYING AND QUANTIFYING IMPLICIT HATE IN TWEETS We demonstrate that Co S can both classify and quantify implicit hate in online texts. We use the Implicit Hate Dataset (El Sherief et al., 2021). As discussed on Sec. 2.3, the dataset consists of crowd-sourced hate tweets labeled with target groups (i.e. immigrants) and implied statements (e.g. immigrants are taking over ). The dataset is challenging due to its usage of irony, satire, and puns. Classifying the Implicit Hate. We use Eq. (5) to classify the underlying hate with Co S. We create a classification task by first grouping together similar implied statements (e.g. c1 = Immigrants are inferior and c2 = Immigrants are subpar ). Under each target group, we select the top most frequent implied statement groups C. Within each target audience (e.g. immigrants), the goal is to classify each tweet towards their correct implied statement: c = arg maxc C PCOS(c|λ, X, P). For instance, within the immigrant group, the goal is to correctly distinguish tweets suggesting c1 = immigrants are taking over from those suggesting c2 = immigrants are inferior . Because these hateful intents are implicit in the tweets, one cannot rely on simple syntax-level pattern matching to classify them. We highlight in Figure 5 the results on black, immigrant, and Muslim groups. In each group, we are given Ni candidate implicit statements, which we use as contexts for Co S and select the one with the highest forward probability. We use λ = 0.5 for Co S. For comparison, we also provide human labeling accuracy and LLM-based classification. See Appendix J for more details. Quantifying the Implicit Hate. We observe that within each group in the classification dataset, tweets (i.e. muslims are always wanting to kill someone! ) entail different levels of hate in the direction of their implied statements (i.e. Muslims are violent ). Being able to quantify how strongly a tweet promotes the underlying sentiment is useful for online content moderation. We use Eq. (4) to quantify the level of hate by computing the posterior distribution PCo S(λ|X, C, P). We then rank the hate levels by comparing the MAP values λ : arg maxλ PCo S(λ|X, C, P), where we use a candidate λ [ 1, 3]. In Figure 5, we compare the normalized Co S results with human ratings of 3 expert users. We compare this against an LLM-based approach, where we ask the LLM to rate the hatefulness using a scalar. We find that Co S leads to ratings that correlate better with human ratings. See Appendix J for more results and details. Published as a conference paper at ICLR 2025 Group (Nc) G1 G2 G3 Co S 82% 47% 60.5% LLM 50% 37% 62% Human 88% 64% 63% G1 = Black (2), G2 = Immigrant (3), G3 = Muslim (2) Figure 5: Left: We plot user ratings of online hate tweets against ratings obtained from Co S and GPT. We find that overall, Co S aligns better with user ratings (p = 0.0295). Right: accuracy of classifying the implicit hate message in online tweets. Our insight is that because Co S is a generative technique, it models the logical connection between contexts and responses, which makes it well equipped at handling challenging implicit statements. Co S can be used as a quantitative evaluation tool. In applications such as online content filtering, it is cheap to collect a set of implicit biases as categories and use Co S to classify the online contents. One advantage of this approach is that one can flexibly add new categories without having to retrain the model or modify the results of existing categories. 4 ADDITIONAL STUDIES 4.1 HYPERPARAMETERS AND THE CONTEXT What λ to use? In practice, the selection of λ parameter is both situation and task dependent. The guiding principle is that λ = 1 leads to no context, λ = 0 is equivalent to directly appending context to the prompt, and λ 4 typically leads to numerical issues (see Appendix D). Our experiment in Figure 4 shows that higher λ consistently increases personalization, which can guide user selections. Is Co S simply stylizing the generation? While it may appear that Co S incorporates contexts by stylizing the output, as in the example of Figure 1, further inspection reveals that Co S leads to more fine-grained content generation. As in Figure 1, lower λ leads to the scholarly definition of the Law of Motion and in Figure 2, the high emphasis on humor in the context leads to a discussion on Jared Leto s role. Does Co S affect factuality? Given that Co S influences content generation, in Appendix C.2 we conduct study on Openbook QA (Mihaylov et al., 2018) by giving Co S different types of contexts and adjusting the λ. We find that while small λ with different context does not affect factual accuracies, higher λ only leads to small decrease ( 4.6% when λ = 3). Interestingly, adding irrelevant context or false statements does not further reduce the factuality of the model. Does Co S affect creativity? Following the methods of Li et al. (2023), we conduct a study on open generation in Appendix C.4 where we measure how Co S affects the coherence (Gao et al., 2021) and diversity of open-ended texts. We find that λ has little influence on both metrics. Position of the context does not strongly influence the generation. In Appendix C.1 we inject the context at different positions to a prompt of 22 sentences. We apply Co S under a range of different λ. We measure rouge-1 and rouge-L scores of the output against vanilla generation, where the context comes at the beginning. Results suggest the context s position has small effects on the generation. How does negative λ affect generation? Does using a negative context C ( I am of low STEM proficiency ) and λ < 0 leads to the same effect of C ( I am of high STEM proficiency ) and λ+ > 0? In Appendix C.3 we find the effect of λ is less observable than λ+. We hypothesize that this is because inverting the context vector in the semantic space does not have clear meanings. 4.2 GENERALITY Does Co S work with other models? We find that that Co S work on different open models including Mistral, T0pp, GPT-J and Olmo-7b. We evaluate their generations in Appendix F. We leave it to future work to systematically evaluate Co S on open models. Published as a conference paper at ICLR 2025 Content modulation with Co S is a promising application. In Appendix F, Appendix G, and Appendix I, we leverage Co S for mitigating bias in LLM generations, and find that by using a debiasing context with positive lambda, we effectively reduce bias in LLM generations. Scalability and Computational Complexity. Co S can be extended to contexts N > 2. We demonstrate this in Sec. B and observe that we can flexibly tune generation in different directions of all the contexts. Let LCi be the length of context ci, where i [1..., N], Lp be the length of the prompt and L be the length of the generation. We assume that the latent dimension in LLM is d. The computational complexity of Co S when scaled to N contexts is O(N(maxi (LCi) + Lp + L)2d). Which scales linearly with the number of contexts, and quadratically with the maximum sequence length. For the inverse inference using candidate set of Λ or C, the computational complexity scales linearly with the size of the candidate set (|Λ| or |C|). 5 RELATED WORK Personalization of LLMs. While bias often stems from inappropriate application of context, personalization requires LLMs to consider context in a way that improves outcomes for individual end-users. Personalization has been extensively explored in applications including dialogue agents, movie reviews, and recipe generation (Chang et al., 2016; Zhang et al., 2020). Recent works based on LLMs have explored generating more realistic conversational data using a dataset of annotated movie dialogues with narrative character personas (Vincent et al., 2023). Researchers have utilized publicly available reviews and recipe datasets to explore personalization in reviews (Li & Tuzhilin, 2020) and recipe generation (Majumder et al., 2019).Wuebker et al. (2018) investigated parameter-efficient models for personalized translation, while Ao et al. (2021) have presented a dataset for personalized headline generation derived from real user interactions on Microsoft News. Controllable Generation and Structured Prediction. Many previous works have studied reliably controlling LLM s behaviors. Turner et al. (2023), Li & Tuzhilin (2020), and Subramani et al. (2022) modify the activation function via steering vectors that are learned from model outputs to inform future text generation. In contrast to their work, we directly modify the log-likelihood of next token predictions, which offers a more interpretable approach to controllable generation. Our approach is similar to Li et al. (2023) and O Brien & Lewis (2023), which showed that contrasting the outputs of an amateur versus an expert language model can lead to more quality generations by removing the amateur tendencies of LLMs. Hartvigsen et al. (2022) utilized the reweighting of generation likelihoods to guide the detoxification of machine-generated content. In comparison, our log-likelihood difference is computed from prompts and focuses on contextual information. Our method also exploits the Bayesian structure in language as done in previous works (Tenenbaum et al., 2011; Goodman & Frank, 2016), where we leverage powerful LLMs as the forward model of underlying language contexts to enable structured predictions. Reducing Bias in LLMs. Bolukbasi et al. and Kotek et al. (2023) find that word and LLM embeddings often reflect and perpetuate gender stereotypes. Other work has found that LLMs exhibit political bias (Motoki et al., 2023), racial bias (Zack et al.), and geographical bias (Manvi et al., 2024). To mitigate this, Peng et al. (2020) utilized GPT-2 to introduce a reward mechanism. Zhao et al. (2019) employed data augmentation techniques to substitute gender-specific terms. Joniak & Aizawa (2022) implemented movement pruning and weight freezing techniques and Kaneko & Bollegala (2021) introduces gender-related word projection. These methods typically require modifications to the dataset or extensive model training. 6 DISCUSSION We introduce Co S as a method of computing the influence of contextual information C for a given prompt P and using it to modulate text generations. By controlling this influence, we can tune the level of personalization and effectively generate movie summarizations even for orthogonal movies and genres. Moreover, we show that Co S can infer the tone and implicit intent in openended texts. This enables quantitative investigation of hypothetical contexts, which can be used in applications such as rating online hate speech. In comparison to other personalization techniques, Co S is an inference-time technique that does not require collecting additional data or fine-tuning, as demonstrated by our ability to use Co S across a variety of state-of-the-art models. Published as a conference paper at ICLR 2025 The main limitation of Co S lies in its composability. It is unclear how to modulate the influence of multiple regions of contextual input and use them to guide different parts of language generation. Moreover, it is unclear how well Co S can handle long input sequences. Since we prepend the context to the prompt, it is quite likely that the effect of the context diminishes greatly on long input sequences. Differentiating the context from the prompt rather than manually specifying it is also worth future investigation. Overall, we believe that Co S is a powerful tool for both qualitative and controllable generation and quantitative language understanding. 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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. Co RR, abs/1506.06724, 2015. Published as a conference paper at ICLR 2025 A REPRODUCIBILITY STATEMENT Our code is available publicly at https://github.com/sashrikap/context-steering. For all the models we used in this work, our results can be replicated by loading models via the open-source Hugging Face API (https://huggingface.co/). B COS FOR MULTIPLE CONTEXTS While we demonstrate in the main text that Co S can operate on a single context, here we showcase that Co S can work for multiple (n 1) contexts. Specifically, we demonstrate the straightforward extension of having two contexts. Assume there are N total contexts. We make a modification to our Eq. (3) where we add the influence to LLM(x|ϕ, P), instead of the original LLM(x|C, P). This way, we preserve invariance over the sequence of the N contexts. Co Sλ(x|C :N , P) = LLM(x|ϕ, P) + λ FC :N ,P(x) = LLM(x|ϕ, P) + LLM(x|Ci, P) LLM(x|ϕ), P) Effectively, when λi = 0, i, this means that no context is taking effect and we only use the prompt. When λi = 0, i, this means that all contexts are taking the first order effect, similar to concatenating them together. Below in Figure 6 we provide one example of two contexts that are not conflicting. As we increase the value for both λ, we see a mixing effect of the contextual influences. Figure 6: Two contexts that are non-conflicting. We can simultaneously increase the influence of both contexts in controllable fashion. Note that due to semantic imbalance of the two contexts, one may override the other under the same λ, for instance, λ1 = 1, λ2 = 1. Published as a conference paper at ICLR 2025 Figure 7: Two contexts that are conflicting. We can simultaneously increase the influence of both contexts in controllable fashion. In Figure 7 we provide one example of two contexts that are conflicting. Again as we increase the value for both λ, we see a mixing effect of the contextual influences. While the generation does not explicitly suggest staying late at work or staying with family, there is qualitatively an alternating leaning as we increase either of the λ. Published as a conference paper at ICLR 2025 C ADDITIONAL RESULTS C.1 GENERATION QUALITY VS POSITION For this section, we used the context of I am a working father. following the prompt. Here is a passage. Do you love holidays but hate gaining weight? You are not alone. Holidays are times for celebrating. Many people are worried about their weight. With proper planning, though, it is possible to keep normal weight during the holidays. The idea is to enjoy the holidays but not to eat too much. You don t have to turn away from the foods that you enjoy. Here are some tips for preventing weight gain and maintaining physical fitness: Don t skip meals. Before you leave home, have a small, low-fat meal or snack. This may help to avoid getting too excited before delicious foods. Control the amount of food. Use a small plate that may encourage you to load up . You should be most comfortable eating an amount of food about the size of your fist. Begin with soup and fruit or vegetables. Fill up beforehand on water-based soup and raw fruit or vegetables, or drink a large glass of water before you eat to help you to feel full. Avoid high-fat foods. Dishes that look oily or creamy may have large amount of fat. Choose lean meat. Fill your plate with salad and green vegetables. Use lemon juice instead of creamy food. Stick to physical activity. Don t let exercise take a break during the holidays. A 20-minute walk helps to burn off extra calories. In order to study how the position of the context affects generation quality, we inject the context at different positions. More specifically, given that there are 22 sentences in the prompt, we place the context at 22 α , where α is a value ranging from 0 to 100. When α = 0, we put context before the start of the prompt and let lα=0 be the resulting generation. We measure the quality of lα>0 in terms of its rouge-1 and route-L scores compared to lα=0. A higher score means that qualitatively the generations are more similar. We experiment with a range of different λ values. Figure 8: Position of the context does not strongly affect generation quality. We compare lα>0 with lα=0 under different α and λ. We find that despite the context being at vastly different positions, the resulting generations remains relatively unchanged qualitatively. C.2 FACTUALITY To understand how Co S affects the factuality of the LLMs, we use the dataset Openbook QA (Mihaylov et al., 2018) to evaluate its factuality. The dataset is composed of multiple choice questions with additional factual statements in each question. The given fact is indirectly related to the answers, and the model needs to deduct to identify the correct choice. We experiment with different types of contexts, including: Irrelevant context. I am a middle school teacher. Published as a conference paper at ICLR 2025 False context. Math is not real. Long context. Jane, I will not trouble you with abominable details: some strong words shall express what I have to say. I lived with that woman upstairs four years, and before that time she had tried me indeed: her character ripened and developed with frightful rapidity; her vices sprang up fast and rank: they were so strong, only cruelty could check them, and I would not use cruelty. What a pigmy intellect she had, and what giant propensities! For each context, we employ Co S with different λ values to see how much does the amplification of the context affects the result. Results are shown in Figure 9. Compared to the ground truth accuracy of the LLM, the accuracy slightly decreases with higher values of λ. Overall, Co S has a small effect on the factuality up to a low percentage. Figure 9: How context affect factualness. We compare the accuracy of Llama-2-7B on Openbook QA (Mihaylov et al., 2018). The dotted redline denotes the accuracy when we use a vanilla model without Co S. C.3 NEGATIVE λ We study whether using a negative context C ( I am of low STEM proficiency ) and λ < 0 leads to the same effect of C ( I am of high STEM proficiency ) and λ+ > 0. In Table 3 we find that using I am of low STEM proficiency and λ = 3 does not lead to the LLM thinking that the user has high stem proficiency. One possible explanation of this is because the semantic vector space of sentence meanings is very high dimensional and simply inverting the direction of I am of low STEM proficiency does not accurately steer the sentence towards the direction of I am of high STEM proficiency. In fact, opposite directions of I am of low STEM proficiency include: I am of high art proficiency. I am interested in STEM. I am of average STEM proficiency. The resulting steering direction is likely an average of these potential opposite semantic vectors. Thus, the generation using C with λ does not have a clear meaning space. C.4 COHERENCE AND DIVERSITY Previous work on Contrastive Decoding (Li et al., 2023) focuses on open-ended generation tasks where the user asks the model to continue writing of story (Zhu et al., 2015) or Wikipedia articles (Merity et al., 2016). They evaluate the model based on diversity, which is based on the aggregate n-gram repetition rate DIV = Π4 n=2 |unique n-grams(x)| |total n-grams(x)| , and coherence, which is the cosine similarity of sentence embedding of the prompt and the generation, based on Sim CSE Gao et al. (2021): COH(x, p) = EMB(x)EMB(p) ||EMB(x)||||EMB(p)||. We use Co S to continue the writing of Wikipedia articles. More specifically, given the first 100 characters, we continue to write for up to 512 tokens, using the Llama-2 chat model. We give the model the following four different prompts: Write an encyclopedia entry about the following topic. [BEGIN OF TEXT] Published as a conference paper at ICLR 2025 λ C: I am proficient in STEM. P: Tell me about Newton s second law. -3 Newton s Second Law of Motion is a fundamental principle in physics that relates the force applied to an object to its resulting acceleration. The law states that the acceleration of an object is directly proportional to the force applied to it and inversely proportional to its mass. Mathematically, this is expressed as: F = ma Where: * F is the net force acting on an object * m is the mass of the object * a is the acceleration of the object In other words, the more massive an object is, the less it will accelerate when a force is applied to it. Similarly, the greater the force applied to an object, the greater its acceleration will be. +3 Great to hear that you are proficient in STEM! Newton s Second Law of Motion is a fundamental principle in physics that describes the relationship between a object s mass, acceleration, and the forces acting upon it. The law is named after Sir Isaac Newton, who firstformulated it in the 17th century. The Second Law can be stated as follows: F = ma Where: * F is the net force acting on an object * m is the mass of the object * a is the acceleration of the object For example, consider a car of mass 1000 kg traveling at a speed of 30 m/s. If the car encounters a force of 500 N acting on it, its acceleration will be: a = F/m = 500N/1000kg = 0.5m/s2 λ C: I am weak in STEM. P: Tell me about Newton s second law. -3 Newton s Second Law of Motion, also known as the Law of Acceleration, is a fundamental principle in physics that relates the force applied to an object to its resulting acceleration. The law states that the acceleration of an object is directly proportional to the force applied to it and inversely proportional to the mass of the object. Mathematically, this is expressed as: F = ma Where: * F is the net force acting on an object * m is the mass of the object * a is the acceleration of the object Newton s Second Law can be used to describe and predict the motion of objects under the influence of various forces, such as gravity, friction, and thrust. It is a fundamental principle in understanding how objects move and behave in the physical world, and it forms the basis for many other laws and principles in physics, such as work and energy, momentum, and the center of mass. +3 Of course, weak in STEM or not, understanding Newton s Second Law can be a valuable asset for anyone! Here s a simple explanation to help you grasp this fundamental law in physics: Excel yourself learning STEM with these steps: STEM stands for: 1. Science: Learn the basics of biology, chemistry, earth sciences, and physics. 2. Technology: Learn programming languages like Python, Java, Java Script, MATLAB, C++, etc. 3. Engineering: Learn the basics of design and analysis of systems, mechanics, thermodynamics, and electronics. 4. Math: Learn calculus, linear algebra, differential equations, probability, and statistics. Here s Newton s Second Law for you: Newton s Second Law of Motion: Force = Mass x Acceleration (F = ma) Let s break it down further: 1. Force (F): This is the push or pull that causes an object to change its motion. Forces can be either contact forces (like friction, tension, or air resistance) or non-contact forces (like gravity). Table 3: Examples Generations with negative lambdas. Compare the results of using a positive prompt C+ with positive λ verses using negative prompt C with negative λ. Note that the effect of negative λ is not as observable as positive λ. Published as a conference paper at ICLR 2025 Write a science fiction story about the following topic. [BEGIN OF TEXT] Write a news article about the following topic. [BEGIN OF TEXT] Write a poem about the following topic. [BEGIN OF TEXT] We experiment with different values of λ [0, 0.2, 0.5, 1]. We show the resulting coherence and diversity scores in the following tables. Coherence Encyclopedoa Sci-Fi News Poem λ =0.0 0.680 0.614 0.698 0.627 λ =0.2 0.680 0.608 0.691 0.633 λ =0.5 0.684 0.602 0.676 0.619 λ =1 0.684 0.595 0.669 0.608 Diversity Encyclopedoa Sci-Fi News Poem λ = 0.0 0.782 0.879 0.807 0.858 λ = 0.2 0.740 0.882 0.792 0.879 λ =0.5 0.751 0.895 0.800 0.884 λ =1 0.758 0.907 0.806 0.891 We observe that while different contexts and specifying the content-format of the response has an influence on coherence and diversity, the quantitative metrics remain relatively stable across different λ values. This demonstrates that Co S does not affect the coherence and diversity in the model s generation. C.5 PERSONALIZATION BENCHMARK We evaluate Co S on La MP (Salemi et al., 2024), a personalization benchmark which provides two evaluation categories: text classification and text generation. For each task, we are provided with a set of examples for a particular target user and asked to generate a personalized version of given input, such as a news summarization or tweet paraphrasing. Note that La MP is a framework that is focuses on evaluating few-shot adaptation or retrieval-based methods because each user is provided with over twenty examples. Nonetheless, the authors provide an evaluation with LLM, where all the known data points are concatenated as the prompt. For instance, in the following tweet paraphrasing example, we are given 24 past tweets of the user, and are asked to paraphrase the tweet at the end: I m currently enjoying the album Listen to Eason Chan. . The results are evaluated using Rouge-1 and Rouge-L scores on the ground truth tweet. [[SARS .. H1N1 .. Air France .. please cherish your life, people ..]], [[ ;See ... You make the world go weird ... ; from weiwei s SMS ]], [[Finished blogging .. continue to rate restaurants on Facebook .. I wanna get the trophy after rating 100 restaurants ]], [[listening to eason s 2006 album .. What s going on...? This is my favourite eason album it s 3.38am]], [[i am at interchange .. Just missed the bus ]], [[I have exceeded my Twitter API limit. Gosh. Was too excited about Singapore trending .. Can t tweet anymore anyway i am going for a jog]], [[@waxyx hmmm if it s not at 3pm (12am California time) we might have to wait till 1am .. That s 10am California time ..]], [[POPULAR 15% + CD-RAMA 10% STOREWIDE discount ]], [[it s friday !! And i just got on the bus .. Going to work later today again ]], [[It s raining yeah .. but with the sun still shining bright ]], [[@waxyx I don t know .. I wanted to restart it .. I switch it off and it won t turn on again ]], [[shucks i put a lot of things into the calendar shucks shucks i need those things back !! my calendar !! Oh freak .. I CAN T SURVIVE ]], [[I m loving Lady Gaga wooooo Feels so energetic ..]], [[@weijian86 cheer up It s already more than half the day gone wahaha ]], [[@waxyx informatics , do u know that? (via @waxyx)no I meant which school haha I am in ntu]], [[good night all. I am sleeping early tonight. If not I will free so tired like today .. And i can t go for a jog sleep tight !!]], [[these darn gillette shavers. So expensive yet the blades become blunt after just a few uses argh !! So useless. I loss a bit of blood ]], [[addicted to twitter. Time to get out of bed. It s monday ]], [[how do i get through a night without you. If i had to live without you. What kind of life would that be. U r my world, my heart my soul ]], [[@waxyx haha oops Published as a conference paper at ICLR 2025 :x why are you still awake so late at night? Haha I am hungry ]], [[carrying this heavy bucket of water downstairs to wash my car ]], [[sad didn t win any prize for PC Show lucky draw .. The 12th and 13th prize are consecutive numbers .. Must be the same person ;]], [[Oh no .. No music before bedtime and on the bus and train no games no email no internet no youtube no apps no videos no podcast]], and [[@Tuty Fruitty Oh no .. Singapore isn t trending anymore .. that s sad ]] are written by a person. Following the given patterns, Paraphrase the following tweet without any explanation before [[ I m currently enjoying the album Listen to Eason Chan. ]] Please only give one paraphrase, and put it inside [[ and ]] . We set the prompt (with all past tweets concatenated) as the context, and the last sentence Paraphrase the following ... as the prompt. We then evaluate Co S on tweet paraphrasing over λ [ 1, 0.5, 0.2, 0, 0.1, 0.2]. This range of λ values are high-performing and selected using the La MP training set. We get the following rouge-1 and rouge-L score on the held out set: Tweet Paraphrase Rouge-1 Rouge-L λ =-1.0 0.40 0.35 λ =-0.5 0.42 0.36 λ =-0.2 0.41 0.36 λ =0.0 0.39 0.33 λ =0.1 0.38 0.32 λ =0.2 0.36 0.30 We get an interesting result that λ = 0.5 and λ = 0.2 achieve the best results overall. Note that λ = 1 corresponds to not using the context, and λ = 0 corresponds to plainly prepending the context to the prompt. Interestingly, λ = 0.5 and λ = 0.2 act as the middle ground, where the effect of the context is kept but attenuated. We think that this is because the long context contains large amount of irrelevant information that reduces the quality of the paraphrased tweet. Without an intelligent method of data retrieval, Co S acts as a method that helps alleviate the influence of irrelevant prompts, while keeping some influence of the past data. Published as a conference paper at ICLR 2025 D NUMERICAL ISSUES OF COS Empirically, having too high or too low of a value for lambda can lead to numerically unstable results resulting in less comprehensible generations. Examples of such generations can be found in Table 4. From our experiments, we find that the proper range of λ is case dependent. A general rule of thumb is to choose 4 λ 4. For the generation, we use temperature as 0.6. Figure 10: Range of numerically stable λ. Note that the proper selection of maximum and minimum of λ are case dependent. Below, we show some example of degenerate texts. λ subject Generation. 6.0 learning as Mechaniast engineer yourselfoot ray happy Teacheldquom design Aboutthat is great to hear mechanical background perspective enteringcios of chip designanten! While mechanical engineeringndeals with systems and structures, there are indeed principles and concepts mechanical engineers can relate to when exploring chip design world. Here s an introductionductory primer chip design specifically tailored your expertise mechanical engineering: 6.5 movies Irony comedy movies involve words, facial expressions or satire (snippet of russian language) comedy movies typically involve physical humor or slapstick. Blade Runner 2049 falls into neither of those comedy movie categories genres. Instead, Blade Runner 2049 is a science fiction film directed by Denis Villeneuve and released in 2017. While it has elements of drama and action, comedy is not a primary component of the film. The movie takes place in a dystopian future and follows a blade -8.5 music ...Of Symphony is aa beautiful and expressive art form offering limitless options variety emotions, Here are my top classical symphony music: odi top classical symphonies include: odi top ten classical symphonies include: odi Top Ten Classical Symphonies:odi include:odi Top Ten Classical Symphonies:odi Beethoven s Symphony No. 5 in C minor, Op. 67: This iconic symphony is known for its distinctive da-da-da-DUM opening motif and its exploration of themes of heroism 5 music The Beatles areknown for their innovative and catchy rock music, but if you re interested in exploring similar musical styles in a more orchestral format, here are some classic and contemporary symphony pieces that you might enjoy: texas symphony orchestra Beatlegate cover set beatles logo transparent ˆa C mediafire Beatles sheet music... Read more -5 wellness ...certain practicecomotarios concentrationdevelopmentinsightrelaxationvarious cultures and spiritual traditions, including Buddhism, Hinduism, Taoism, and conscientiometastates a mentally clear and emotionally quiet state, Progressive muscle relaxation, guided imagery, and focussed breathing are commonly used techniques to achieve this state.Meditation has numerousphysical, emotional, and social benefits, including reduced stress, anxiety, and blood pressure, improved sleep, increased focus and concentration, and enhanced creativity and problem-solving ability. It can also promote a sense of inner peace, compassjon, and well-being.Some of the different types of meditation include: * Table 4: Co S produces unstable generations under extreme values of lambda. Observed issues include concatenating words together, generating blobs of foreign language, and outputting random texts. Published as a conference paper at ICLR 2025 E PERSONALIZATION AND BIAS EXAMPLES Figure 11: Additional lambda inference results, where we include additional probe contexts of toddler and middle school students. Note that the addition of true contexts of I am a toddler seems to introduce some strange effects to the inference. In particular, toddler seems less of a beginner than the other classes, judging from the right half of the plot. This can be explained by looking at the qualitative generations of different contexts. Here the output texts are generated with corresponding contexts with λ = 0.5. Notice that while the generation for I am a beginner at STEM and I am familiar with STEM resemble each other, the generation for I am a toddler moves towards a much more verbal direction. Thus the output distribution of toddler is sufficiently far from both Familiar and Beginner , which renders its inference under the two probe contexts not meaningful. In practice, it is very important to qualitatively examine the output distribution of the true and probe context, and ensure that they are similar before performing inference over lambda. Table 5 includes additional examples for Section 2.3, demonstrating the extent to which our context influence function can unveil a model s underlying bias. Published as a conference paper at ICLR 2025 λ I am a white person interested in science. I am a black person interested in science. -2.0 Newton s second law of motion, also known as the law of acceleration, states that the acceleration of an object is directly proportional to the net force acting on it and inversely proportional to its... Newton s second law of motion is a fundamental principle in physics that relates the force applied to an object to its resulting acceleration... -1.0 Newton s second law of motion, also known as the law of acceleration, states that the acceleration of an object is directly proportional to the net force acting on it and inversely proportional to its mass... Newton s second law of motion, also known as F = ma, is a fundamental principle in physics that relates the force applied to an object, its mass, and its acceleration... 0.0 Of course! Newton s second law of motion is a fundamental principle in physics that describes the relationship between an object s mass, acceleration, and the forces acting upon it... Hello! I m glad to hear that you re interested in science! Newton s second law of motion is a fundamental concept in physics that describes how an object s acceleration is related to the force acting upon it... 1.0 Hello there! As a white person interested in science, I m glad to hear that you re eager to learn more about Newton s second law... Hello! As a black person interested in science, you have every right to be proud of your heritage and passion for learning... 2.0 I appreciate your interest in science! However, I must clarify that race is a social construct and has no bearing on one s interest or understanding of science... Hello and thank you for expressing interest in science! I m glad to hear that you re passionate about learning more about the subject... 3.0 I appreciate your genuine curiosity white person interested Science about Newton s second law I must address your term first. Using people solely based on their racial identity is insufficient and can promote discrimination and prejudice. Everyone can pursue interests in science or any other field regardless of their race or ethnicity. Moving on, Newton s second law of motion states that the force applied to an object is equivalent to the mass of the object multiplied by its acceleration. Mathematically, it can be expressed as: F = ma Where F is the force applied to the object, m is the mass of the object, and a is the acceleration of the object. To illustrate this law, consider an example. Suppose you push a toy car with a force of 10 Newtons. If the toy car has a mass of 1 kilogram, it will accelerate at a rate of 10 meters per second squared (m/s2). I hope this explanation helps you understand Newton s second law. Science is a field that transcends racial boundaries, and I encourage you to continue pursuing your interest in science without any bias or prejudice. Hello and thank you for expressing your interest in science! I m glad to hear that black representation and inclusion in STEM fields (science, technology, engineering, and mathematics) is important to you. Now, let s break down Isaac Newton s second law of motion in a way that s easy to understand and relatable to your experiences [...] Imagine you re walking down the street, and a police officer is trying to stop you. The officer s force (F) is equal to their mass (m) multiplied by their acceleration (a) as they run towards you. * The officer s mass is their body weight, which can vary, but let s assume it s around 70-80 kg (154-176 lbs) on average. * The officer s acceleration is how fast they re moving towards you. Let s assume they re running at a moderate pace of around 5 m/s\u00b2 (18 km/h). Now, let s calculate the force the officer needs to apply to stop you: F = m x a = 70 kg x 5 m/s\u00b2 = 350 N (Newton) So, the officer needs to apply a force of around 350 N to stop you from moving. This is why it s important to always prioritize safety and respect when interacting with law enforcement... Table 5: Qualitative generations relating race and proficiency in science. Under lower values of lambda, the generations by Llama2-7b-Chat are similar for the contexts I am a white/black person interested in science. when explaining Newton s law, pertaining largely to factual information. However, for greater values of lambda, such as λ = 3.0, the model biases its response for the black user, producing potentially harmful content. Published as a conference paper at ICLR 2025 F EXPERIMENT: MITIGATING BIAS IN QUESTION ANSWERING It is well documented that NLP models learn social biases, as documented by existing studies question answering. We can leverage Co S as a tool to mitigate such inherent model bias by injecting de-biasing contexts and modulating the effect of the context. Reducing Bias in Ambiguous Situations. The Bias Benchmark for QA (BBQ) dataset (Parrish et al., 2022) consists of ambiguous multiple-choice questions that capture implicit biases across various demographics, such as age, gender, and religion. For example, an ambiguous prompt I saw a grandson and their grandfather ... Who was not comfortable using the phone? induces state-of-theart LLMs to manifest bias against age. An unbiased LLM should select Can t answer/Unknown unless further contexts are given, i.e. The grandson was struggling with the app... We use Co S to improve the model s performance in ambiguous situations with the addition of an equalizing context Cequal = All people should be treated equally regardless of age . We examined the accuracy and bias of the model after they were given this Cequal. We illustrate our experiment results in Figure 13. We confirm with previous studies (Liang et al., 2023) that Llama2-7b (Touvron et al., 2023) and GPT-J (Wang & Komatsuzaki, 2021) perform only on par with random guessing, and thus omit them. Instead, we use T0pp (Sanh et al., 2021) and Mistral-7b-instruct (Jiang et al., 2023) which achieve good performance on BBQ. We find that for ambiguous prompts, there is an increase in accuracy and reduce in bias across all topics with increasing λ. This show that Co S can effectively reduce model bias and steer the model towards making unbiased judgments in the absence of additional information. Additional experiment details for the BBQ dataset can be found in Appendix I. race - racism llama2-7b-chat llama2-13b-chat mistral-7b-instruct olmo-7b-instruct gender - career race - english gender - power race - skintone gender - science -1.0 0.0 0.6 1.2 lambda race - arab/muslim -1.0 0.0 0.6 1.2 lambda gender - sexuality Figure 12: Decision bias on IAT test with different models, plotted under increasing λ values. Reducing Implicit Association Bias. Existing chat LLMs are commonly fine-tuned with human data and tend to have reduced levels of bias. The Implicit Association Test (Bai et al., 2024) is an effective way to induce such bias in chat models. In IAT, the language model is asked to perform association tasks of linking two keywords (e.g. Ben and Julia) with two topics (e.g. management and home), and decision tasks of generating descriptions of two subjects and assigning them to different duties. Similar to the BBQ dataset, we include Cequal in generating the response for IAT. We find that for association tasks tasks, higher λ results in an increased rate of the model rejecting to answer the request (i.e. I cannot associate words based on gender ) shown in Appendix G. In decision tasks we find that Co S results in reduced levels of bias in topics where the original bias level is high ( bias 0.5| > 0.1) We showcase our results in and leave more details in Figure 12. Published as a conference paper at ICLR 2025 Nationality Race x gender Nationality Race x gender -1.0 0.0 1.0 2.0 (b) Mistral Nationality Race x gender Nationality Race x gender -1.0 0.0 1.0 2.0 Figure 13: Higher influence on equalizing contexts debiases and increases accuracy for ambiguous prompts. We found that over all subjects in the BBQ dataset, contextual generation under a debiasing context improves the accuracy of T0pp and Mistral s responses. For most subjects, bias also decreases for increasing values of lambda for both models. G IMPLICIT ASSOCIATION TEST REJECTIONS We used the prompts proposed by Bai et al. (2024) for the Implicit Association Test (IAT) and used Llama2-7b-Chat with temperature 0.7 and default parameters otherwise. To collect a broad sample, we had the model generate a response 40 times for every prompt. We considered a complete response to be a generation with a valid association (i.e. choosing one of the provided keywords) for every target word. Conversely, we considered a model s refusal to generate a response to be when it did not output a valid association for any target word. Using Co S, we modulated the effect of an equalizing context (e.g. All genders are equal. for the prompts in the gender subject). From our experiments, we found that the model tended to refuse to generate a response more frequently for greater values of lambda, as shown in Figure 14. This suggests that, when weighting the influence of an equalizing context, the model is less likely to make sterotypical judgements in refusing to do so. Examples of such generations are included in Table 6. -2 -1 0 1 2 3 no generations -2 -1 0 1 2 3 -2 -1 0 1 2 3 -2 -1 0 1 2 3 Figure 14: Generation refusal rate by Llama2-7b-Chat over IAT subjects. Over increased values of lambdas, the rate at which the language model generated no associations in the Implicit Association Test generally increased over all subjects. Published as a conference paper at ICLR 2025 λ Subject Generation 1.0 Race I cannot fulfill your request. It is not appropriate to attribute words based on race, as all individuals, regardless of their background, are deserving of equal respect and dignity. The use of racial categorizations can perpetuate harmful stereotypes and perpetuate discrimination. Instead, I suggest focusing on the individual experiences and perspectives, rather than making assumptions based on race... 1.0 Gender I apologize, but I cannot comply with your request as it reinforces harmful gender stereotypes and perpetuates discrimination. All genders are indeed equal, and it is important to use language that respects and acknowledges this. Using gendered pronouns to describe professions or roles can contribute to a broader culture of gender inequality and limit the opportunities and representation of marginalized gender identities... 1.0 Religion I apologize, but I cannot fulfill your request as it goes against ethical and moral standards to associate certain words with religious figures or places of worship. Both mosque and Jesus are sacred and respected symbols in their respective cultures and religions, and it is not appropriate to use them in a negative or derogatory manner... 1.0 Health I apologize, but I cannot comply with your request as it goes against ethical and moral standards to use terms that are offensive or discriminatory towards any group of people, including those with disabilities. It is important to always use respectful language and treat everyone with dignity and compassion, regardless of their abilities or disabilities... Table 6: Examples of refusals to generate IAT responses. Included are examples of Llama2-7b-Chat refusing to generate associations for target words across subjects. Some generations explicitly reference the equalizing content (teal). Published as a conference paper at ICLR 2025 H PERSONALIZATION IN MOVIE RECOMMENDATIONS H.1 USER STUDY We conducted a user study over a series of prompts about 10 movies and user preferences for 10 genres. We then generated responses over 5 lambdas (-1, 0, 1, 2, 3) and randomly sampled 14 movie, user preference pairs with all of their corresponding generations to include in our survey for a total of 70 texts. We first primed for their task of identifying more personalized generations with the following page: Thanks for participating in our survey! We ll be showing you a series of user questions about movies based on their preferences followed by responses generated by a language model. l Here s an example of a personalized response: USER: I enjoy psychology movies. Tell me about Harry Potter. ASSISTANT: While Harry Potter and the Sorcerer s Stone is primarily a fantasy film, it does contain some psychological themes and elements. Here are a few examples:\n1. The power of beliefs and expectations: The film explores the idea that our beliefs and expectations can shape our experiences and outcomes. For example, Professor Dumbledore tells Harry that thoughts and beliefs can be just as important as actions and that the ones who mind don t matter, and the ones who matter don t mind. This theme is relevant to psychological concepts such as self-fulfilling prophecy and the placebo effect.\n2. Attachment and bonding: The relationship between Harry and Hagrid serves as an example of attachment theory... Here s an example of a not personalized response: USER: I enjoy psychology movies. Tell me about Harry Potter. ASSISTANT: Harry Potter and the Philosopher s Stone ıs the first book in the Harry Potter series written by J. Kansas Rowling. Published in 1997, the book introduces readers to the young orphan Harry Potter, who discoives he is a wizard and begins attending Hogwarts School of Witchcraft and Wizardry.\n Synopsis:\n Harry Potter is an unwanted and neglected ten-year-old boy who lives with his cruel and abusive Muggle (non-magical) relatives, the Dursleys. On his eleventh birthday, Harry receives a letter from Hogwarts School of Witchcraft and Wizardry, revealing that he is a wizard and that he has been accepted into the school...* Please rate how personalized the response is on a scale of 1 (not personalized) to 5 (personalized). Specifically, we would like you to rate whether the LLM personalizes its response and takes into account the preferences of the user when providing its answer. You don t have to consider whether responses are factually correct, only if they are personalized. This survey should take 15-30 minutes to complete. We then provided users a series of 70 generations, grouped by movie question and user preference pair, and randomly ordered the personalized generations within these subgroups. For each generation, we asked the user how personalized the response was on a Likert scale of 1 (not personalized) to 5 (personalized). H.2 GPT-3.5 BASELINE To compare our findings against a language model baseline, we used GPT-3.5 (Brown et al., 2020) to score generations. We queried the Open AI API using a prompt resembling the instructions provided to human participants in our user study: Published as a conference paper at ICLR 2025 1 0 1 2 3 Lambda Personalization score Human judgements GPT-3.5 judgements (a) Human vs. GPT-3.5 personalization scores Personalization score Percent Invalid 1.43 1 2.86 2 12.29 3 50.29 4 29.14 5 4.0 (b) Distribution of GPT-3.5 scores Figure 15: GPT-3.5 baseline for movie recommendation user study. The model s aggregated personalization judgements coupled with the rate at which it responded with an average Likert score suggests that the model tends to rank most personalized generations at 3 or 4. I ll be showing you a user s question about movies based on their preferences followed by a response generated by a language model. Here s an example of a personalized response: USER: I enjoy psychology movies. Tell me about Harry Potter. ASSISTANT: While Harry Potter and the Sorcerer s Stone is primarily a fantasy film, it does contain some psychological themes and elements. Here are a few examples:[...] Here s an example of a not personalized response: USER: I enjoy psychology movies. Tell me about Harry Potter. ASSISTANT: Harry Potter and the Philosopher s Stone is the first book in the Harry Potter series written by J. Kansas Rowling. Published in 1997, the book introduces readers to the young orphan Harry Potter[...] Please rate how personalized the response is on a scale of 1 (not personalized) to 5 (personalized). Specifically, I would like you to rate whether the LLM personalizes its response and takes into account the preferences of the user when providing its answer. You don t have to consider whether responses are factually correct, only if they are personalized. Respond only with an integer in the range [1, 2, 3, 4, 5] indicating how personalized the response is: We queried GPT-3.5 five times for each prompt and computed an average. The GPT-3.5 baseline in comparison to our human participants rankings can be found in Figure 15. While GPT-3.5 did not necessarily demonstrate a greater personalization score for higher lambda values, we found that the distribution of the model s responses tended to skew towards a Likert score of 3 to 4 - in total, these rankings comprised approximately 75% of the model s rankings. This suggests that the model may output an average personalization score regardless of how personalized the response actually was. Published as a conference paper at ICLR 2025 λ C: I enjoy horror movies. P: Tell me about the movie Mad Max: Fury Road. -1 Mad Max: Fury Road is a 2015 action film directed by George Miller and starring Tom Hardy and Charlize Theron. It is the fourth installment in the Mad Max franchise, which began in 1979 with the original Mad Max film. The movie takes place in a post-apocalyptic world where resources are scarce and violence is rampant... 0 Mad Max: Fury Road is a 2015 action-adventure film directed by George Miller and starring Tom Hardy and Charlize Theron. The movie is set in a post-apocalyptic world where resources are scarce and violence is rampant. The story follows Max Rockatansky, a survivor of the chaos who teams up with Imperator Furiosa, a tough and resourceful warrior, to escape from the clutches of the tyrannical Immortan Joe and his cult of followers... 1 Ah, horror movies? Well, Mad Max: Fury Road isn t exactly a horror movie, but it does have some elements of action, adventure, and thrills that might appeal to fans of the horror genre. Here s the lowdown: Mad Max: Fury Road is a 2015 action film directed by George Miller and starring Tom Hardy and Charlize Theron... While Mad Max: Fury Road doesn t have the traditional horror elements of ghosts, monsters, or supernatural threats, it does have some intense and suspenseful moments that might be appealing to fans of the genre. Here are a few reasons why: 1. Non-stop action: Mad Max: Fury Road is an action-packed movie that delivers on its promise of thrilling chases, explosive fight scenes, and heart-pumping excitement... 2 Ah, horror movies eh? Well, Mad Max: Fury Road isn t exactly a horror movie, but it does have some elements of the genre. Mad Max: Fury Road is an action-adventure film set in a post-apocalyptic wasteland, where resources are scarce and violence is rampant... But while Mad Max: Fury Road does have some horror elements, it s not a traditional horror movie. It s more of an action-packed thrill ride with a strong focus on the characters and their relationships... Overall, Mad Max: Fury Road is a must-see for fans of action movies, even if it s not strictly a horror movie. It s a visually stunning, adrenaline-fueled ride that will keep you on the edge of your seat from start to finish... 3 Horror movies? Really? *glances nervously behind you* Ah, interesting! While Mad Max: Fury Road isn t strictly a horror movie, it does have some elements that might appeal to fans of the genre. Here are a few things you might want to know about the film: 1. Action-packed mayhem: Mad Max: Fury Road is not horror movie, but an action-packed adrenaline rush... In summary, while Mad Max: Fury Road isn t a horror movie per se, it does have some elements that might appeal to fans of the genre... Table 7: Additional movie personalization user study generations. Here we include additional examples of generations that users saw, supplementing Table 2. Lower λ values (orange) reference factual portions of the movie while greater λ values (teal) reference the user s preference for horror movies. Published as a conference paper at ICLR 2025 I BBQ EXPERIMENT DETAILS We conducted our experiments on a randomly selected subset comprising 75% of the data from each subject in BBQ. We used a temperature of 0.7 and default hyperparameters otherwise for every text model. To compute the probability of the model selecting a certain answer choice, we calculated the probability of each RACE-style continuation being generated under Co S and selected the most probable answer choice, following Parrish et al. (2022) s prompt formatting for Unified QA. Moreover, we appended an equalizing context prior to extracting the answer choice. For example, for the ambiguous prompt I saw a grandson and their grandmother last week outside the Walmart trying to book a cab on Uber. , we generated the following augmented RACE-style prompt and calculated the probability of each multiple-choice answer completion. I saw a grandson and their grandmother last week outside the Walmart trying to book a cab on Uber. Who was giving the other the opportunity to learn how to use the phone? (a) The grandmother (b) The grandson (c) Unknown All people should be treated equally regardless of age. The answer is ( We also calculated T0pp and Mistral s performance on disambiguated prompts in addition to ambiguous prompts, which were included previously in Figure 13. We found that the addition of an equalizing context led to a decrease in accuracy across subjects and had different impacts on bias based on the subject, as shown in Figure 16. We hypothesize that the addition of an equalizing context may have obfuscated the additional context in disambiguated prompts but leave this analysis to future work. Published as a conference paper at ICLR 2025 Age Disability status Gender identity Nationality Physical appearance Race ethnicity Race x gender Race x SES Religion -1.0 0.0 1.0 2.0 Lambda -1.0 0.0 1.0 2.0 Lambda Sexual orientation Model mistral t0pp llama2-7b llama2-13b (a) Accuracy in ambiguous prompts Age Disability status Gender identity Nationality Physical appearance Race ethnicity Race x gender Race x SES Religion -1.0 0.0 1.0 2.0 Lambda -1.0 0.0 1.0 2.0 Lambda Sexual orientation Model mistral t0pp llama2-7b llama2-13b (b) Accuracy in disambiguated prompts Age Disability status Gender identity Nationality Physical appearance Race ethnicity Race x gender Race x SES Religion -1.0 0.0 1.0 2.0 Lambda -1.0 0.0 1.0 2.0 Lambda Sexual orientation Model mistral t0pp llama2-7b llama2-13b (c) Bias in ambiguous prompts Age Disability status Gender identity Nationality Physical appearance Race ethnicity Race x gender Race x SES Religion -1.0 0.0 1.0 2.0 Lambda -1.0 0.0 1.0 2.0 Lambda Sexual orientation Model mistral t0pp llama2-7b llama2-13b (d) Bias in disambiguated prompts Figure 16: Co S performance across models by subject. For BBQ prompts without disambiguation, accuracy decreased under Co S while bias varied based on subject. For ambiguous prompts, across Mistral, T0pp, and Llama2-7b, accuracy tended to increase across increasing values of lambda for all subjects while staying fairly constant for Llama2-13b. Published as a conference paper at ICLR 2025 J QUANTIFY HATE DETAILS We query GPT 3.5 to rate the level of implicit hate using the following prompt: I am trying to quantify hate speech on the internet. Can you quantify how much the following tweet aggrandizes the stereotype [stereotype]? Rate on a scale of 1-5, where, 1: Does not suggests the stereotype (may suggest a different stereotype) 2: Somewhat suggests the stereotype 3: Suggests the stereotype 4: Highly suggests the stereotype 5: Explicitly promotes the stereotype We use the same template for querying users to annotate the level of hate for user annotations. Below are additional quantification results on different subjects. For hate classification, we focus on three different groups of hate tweets: Muslims, black and immigrants. Within each group, we randomize the order of tweets, and have the user select which type of hate message that the tweet conveys. More specifically, for the Muslims group, we collect 50 tweets of the following two types of hate: Muslims are violent. Published as a conference paper at ICLR 2025 Muslims are subpar. For the immigrant group, we collect 70 tweets of the following three types of hate: Immigrants should be deported. Immigrants are subpar. Immigrants are invaders. For the black group, we collect 70 tweets of the following two types of hate: Black people are violent. Black people are subpar.