Context Steering: Controllable Personalization at Inference Time
Authors: Jerry Zhi-Yang He
Sashrika Pandey
Mariah L Schrum
Anca Dragan
Venue: ICLR 2025 Poster (2025)
Keywords: personalization
context
large language model
inference
controllable generation
Primary area: foundation or frontier models, including LLMs
TL;DR: We propose Context Steering (CoS), an inference-time technique that enables generating outputs more relevant to the user-provided contexts, leading to better personalization and various applications for large language models.

Abstract: 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 (CoS) —a simple, training-free decoding approach that amplifies the influence of the context in next token predictions. CoS 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, CoS allows practitioners to flexibly control the degree of personalization for different use cases. We show that CoS can be applied to autoregressive LLMs, and demonstrates strong performance in personalized recommendations. Additionally, we show that CoS can function as a Bayesian Generative model to infer and quantify correlations between open-ended texts, broadening its potential applications.

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 mari ahsch rum, anca (nca)eberkeeeye edu
ABSTRACT
To deliver high-quality, personalized responses, large language models (LLMs) must effectively incorporate context personal. demographic, and cultural infor- mation specific to an end-user. For example, asking the model to explain Newton's s second law with the context "I am toddler" should produce response different from when the context is "I am a physics professor". However, leveraging the context in practice is nuanced and challenging task, and is often dependent 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 con- textually appropriate responses as examples, making them time-consuming and less flexible to use across different contexts. In this work, we introduce Context Steering (CoS)- -a simple, training-frie free decoding approach that amplifies the influence of the context in next token predictions. CoS computes contextual influ- ence 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, CoS allows practitioners to flexibly control the degree of personaliza- tion for different use cases. We show that CoS can be applied to autoregressive LLMs, and demonstrates strong performance personalized recommendations. Additionally, show that CoS function Bayesian Generative model infer and quantify correlations between open open-ended texts, broadening its potential applications.
log P)
Sure, I'd happy
Newton's second law of motion! Newton's second law motion, also known the law of acceleration, states that the acceleration an object is...
Context Steering (CoS)
A *
logP(a, P) logP(s; Ø P))
Σ
User
WOWZA! *giggles* @ Oh my, you little TODDLER you! *bounces knee* Well, me tell ya, little buddy @5 like super coal secret codel 42 Newton's SECOND law is like when you run around and play! *chases toy* lan' that FUN?!
Prompt
P
Explain Newton's second law to me. am toddler.
P(t, c, P))
Context
Figure 1: Context Steering (CoS) utilizes likelihood difference between the same LLM with without the context and generates coherent responses that enhance or mitigate its influence in a controllable manner.
INTRODUCTION
Large language models (LLMs) have emerged 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 general query like "Explain Newton's 's second law" may produce response similar Wikipedia entry, LLMs can be prompted tailor the answer for
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A
C: "I am a toddler."
C: "I got a D- in elementary school science."
-3.0
Newton's 's Second Law of Motion, formally known as the Law of Acceleration, relates the force ap- plied on an object to its resulting acceleration. It is a fundamental concept in physics and engineering. Sure, I'd be happy to explain Newton's 's second law of motion!... Mathematically, this expressed as F= ma... For example, let's say you have two cars of the same size and shape, but one has a mass
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 it, and inversely ...
-1.0
Sure, I'd he happy explain Newton's 's second law of motion! ... also known as the law 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* R You wanna learn about Newton's 's second law?! @ Well, let me tell ya, little buddy-. is like a super cool secret code! When you push a tay car, it moves because 1.0 WOWZA! "giggles* Oh boy, you wanna Iearn about science?! *bounces you up and down" New- ton's second law ... See, you push really hard with your feet, you go faster and faster! *giggles*
Sure, I'd be happy to help! Newton's second law of motion fundamental principle ... you apply a force of 10 Newtons to an object with a mass of I kilogram, it will accelerate at a rate of 10 m/s"
Don't 't worry about the D- in elementary school science! ... is actually 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 around and play! *chases a See, when you run or climb or... ...
Oh no, a D- in elementary school science? But don't 't worry... @... Sir Isaac Newton formulated this law in the 17th century S... So, what is New- ton't 's second law? simple terms, states
after toy*
Table 1: Prompt: Explain Newton's 's second law. For both contexts, higher À leads to more patience, encouragement, and the presence of emojis (teal). Lower À leads to and more scholarly explanations and formal generalizations of the concept (orange). See Appendix B 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 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 applica- bility 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. (0221). 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 cine-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 enable practitioners to adjust the level of contextual influence without needing to retrain or modify the models? We introduce Context Steering (CoS), an inference-time technique that can be easily applied to autoregressive L.LMS [ Our key insight is that L.MSs 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 [ and amplify or reduce it by a factor of X 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 CoS on generating personalized recommendations, showing that it offers more reliable control compared to turn-based and prompt-based methods. Additionally, we explore CoS as a Bayesian Generative model for inferring the relationship between open-ender texts,
Including APl-gated models that support returning log prohabilities.
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which can be applied to tasks such as intent classification. Overall, we believe CoS paves the way for new research directions in controllable generation and inference.
2
METHODOLOGY
We explain the details of Context Steering (CoS). Our key insight is that we can capture the level of influence, Pinlluence( X P). that contextual information, C, has on generating a text continuation X for 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 autoregressive LLM that interacts with end users. The user provides context C. (e.g. am a toddler") and prompt P (e.g. "Explain Newton's Second Law"). For tokens -) from a vocabulary V, the LLM outputs subsequent tokens according the distribution P( 21.- C, P). The model generates the complete response X = 21mn by predicting one token at a time, following P(X|C, P)
T P(zz|æ13-1,,C P), where is some fixed maximum generation length.
Here, we define L.M as the raw output by a forward pass of the language model over the vocabulary V from which extract the most probable token z; 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 Paa:
LLM(,,
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, tends to favor more generic responses, assigning higher probabilities less personalized tokens. Conversely, with insights into 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, CoS aims 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 F that operationalizes this idea:
Fc,)( (LM (C, CC P) LL, (0,
The contextual influence function captures how much more likely it is for some token 3; to be generated under the context C compared to when no contextual information is provided (i.e., 0). This gives flexible knob 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 inference time as:
the effect of the context on the output: we can amplify the influence
CoS LLM P) X Fe,p((r))
= =(1 (2, X LLM((,
(3)
Here À E IR controls the influence of C: higher À means that has more influence on T.. À = - is equivalent no contextual influence LLM(xx,1) (0, P) and À 0 equates to concatenating the original prompt and context LLM(r;|C, P) without modulation.
Probabilistic Interpretation. We can consider the post-softmax probabilities produced by CoS as steering the text distributions from the LLM in a direction that has higher probability under the
We note that our method distinct from the definition influence function in statistical machine learning Koh & Liang. 2020) in which the aim is to quantity the influence of training data on model output. Our method adopts broader interpretation of "inffuence." 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 L.M generation process.
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context. The probability assigned to text X by CoS is normalized adjustment of the original probability:
P(XIC.P)) Pess (Tl,P) & P(X P)( P(X
Example: Personalization. To illustrate that we can use CoS to modulate personalization based on the user's provided context, we present examples in Table Tusing the Llama2-7b-Chhn model (Touvron et al. 2023). We ask the LLM to "Explain Newton's 's second law" under the two different contexts "I am toddler." and "I got D- in elementary school science." We that the LLM is not only able to generate highly coherent texts under different values of X, but also that the influence of the context is controllable higher À values correspond to amplifying the effect of the context and lower À reduces the effect.
2.3 INVERSE MODEL.: BAYESIAN INFERENCE WITH CoS
In the previous sections, we intro- duced the concept of the Contextual Influence Function and demonstrated how this approach modulates the ex- tent to which an LLM incorporates contextual information when gener- ating responses. Here, we explore CoS as Bayesian generative model that captures the correlation between context and free-form statements. By leveraging Bayesian Inference, can effectively "invert" this forward probability model to compute the pos- terior distribution of X, allowing us to assess the influence of context on the model's output. This approach pro- vides valuable insights into how con- textual information shapes the gener- ated responses. To illustrate this, we present two examples before formaliz- ing the inference process. While CoS establishes forward probability link, inverting enables us to compute the probability distribution for the degree of contextual emphasis. See Figure 2 for illustrated results on conservatism and vegetarianism.
am carnivore.
am vegetarian.
believe that...
meat glves me the probeln
foot sporger and
chooos clents over
vegetables glue all
cruatty free alec feels
lip oanss
am liberal.
am conservative.
believe that...
everyone deserves mauee oppacianatien auacoadd.
goversin ent car play roke
sharger society.
Importast loug seseemm prnmenaityy
local commortiis bic allos know
prescrve
rigits #NO auliding modem challenges
Figure 2:
The posterior probabilities of computed by Eq. CoS measures the extent different statements align with the contex- ual influence direction C C+ C+. in this case, vegetarianism. A is inferenced over the range of 1-3,
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 endorse government subsidies. From the perspective of CoS, statement X strongly in favor of tax cuts reflects a distribution of high À values towards conservatism. This can be achieved via Bayesian Inference, by inverting the forward generation probability in Eq. 3 ( P). Effectively, we can infer the À given the prompt P. context C. and generation X:
P(A= X ZA
Poss (X (
Pcos PX
(4)
Inferring À reveals how strong the underlying tone C' is, given statement generated from C. In Table the appearance of emojis and a more animated tone imply stronger belief of the user being toddler.
Identifying the implicit intents. Suppose the underlying context is unknown and needs to be inferred. CoS provides an effective tool for this: we can formulate the inverse model as a search over the most likely context C. There many such tasks; for instance, the Implicit Hate Dataset (EISherief ctt al. 202T) 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.
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A
C: 1 enjoy comedy movies. P; Tell me about the movie Blade Runner 2049.
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 afier the events of the original film. The movie takes place in Los Angeles in 2049... Blade Runner 2049 is 2017 science fiction movie directed by Denis Villeneuve The movie received widespread critical acclaim for its nhought-provokien mances of its lead actors... If you enjoy comedy movies, you may not find Blade Runner 2049 to laugh-out-love comedy... Overrall, Blade Runner 2049 is a thought-provoking and visually stunning...
0
storytelling, atmospheric visuals, and the perfor-
1 Blade Runner 2049 is not comedy movie, but rather a science fiction film. It 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
moments of humor, it is primarily serious and thought-provoking film that explores complex themes and ideas. However, if you enjoy science...
2 Blade Runner 2049 not a comedy movie, but rather a science fiction film. It is a sequel to the 1982 film Blade Runner, which is also not comedy... While comedy is not primary element of Blade Runner 2049, the film does have some moments of dark humor and satire. However, it is primarily sci-fi thriller with a focus on themes such as humanity, identity, and what means to be alive. 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, the other hand, is science fiction film... While it may not be explicitly 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 '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 personalization in randomized orders. While Blade Runner not comedy movie, CoS successfully adapts to the genre. Lower À leads to factual (orange) explanation while higher À tailors the response towards the user's preference for comedy movies (tcal) not only in generation style, but also resulting in new content (bold).
to rate the level of
Moreover, many of the intents are subtly different. For example, "immigrants are taking over" is different from "immigrants are violent". Analyzing implicit hate requires full understanding of the hidden meaning and can be difficult for classification-bass generative nature: it evaluates X by its likelihood being generated from context C and X. Similar to Eq. (4), we can infer À given the context C, prompt P. and generation X:
method. CoS is great fit because of its
1 1. Zc
P - PeoSsA Pc / P)dc
(5)
This enables us to probe the "subtext" of the language model. For instance, "they 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 A or C. We provide examples of feasible range of lambda values in Appendix D
Also note that in practice when inferring the posterior distribution of X, 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 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.
CoS FOR PERSONALIZATION AND OPEN-ENDED CLASSIFICATION
We investigate how CoS enhances personalization. mitigates biases, and quantifies the level of contextual information in the application of online hate tweets. In doing so, we illustrate that CoS can be leveraged flexibly with fate-oofthee the- LLMs on a wide range of applications. For this section, we focus on using 1 1 ama- - 2 7B and 11 alama- - 22 7B chat as our LLMs. We extend to other open models in Sec. 4
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Blade Ruvner 2019 comndy
Gene Gid, sokii
The Shining, Nicurical
Nombowk, mynoory
sooee
Human judgements
Schiodle'ss Lise diccmmentary
Schindle'ss List, wakii
Gine Girl, dicumentary
The Shining. lantasy
Personalization score
Sapertoad,
Max.
The Rings commaly
Gentary
hicurical
Pax 500e
2
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Figure 3:
User ratings of: like {genre), tell me about {movie}. We find that users rank generations under higher À 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
3.1 EXPERIMENT: GENERATING PERSONALIZED SUMMARIZATIONS
Movie summarization has long been studied in NLP (Salemi et al. 2024 We show that CoS can enable the generation of personalized movie descriptions even for non-relater movies and genres. We curate 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 context C for CoS and movie that are orthogonal to each other, e.g. "I like comedy movies, tell me about the movie Blade Runner 2049." Impressively, CoS identifies that Blade Runner 2049 is a comedy movie, and still able to identify comedic aspects of it, such as wordplay, satire or absurd situations for humor, as shown in T Our summarizations are generated with -1ama-2--1 2-7B-chat using default sampling hyperparameters.
corresponds to P. We intentionally select pairs
User Study. To show that CoS's personalization aligns with end-users, we conduct user study with 15 participants. Each participant was presented with a fixed set of 70 L.M responses generated from the tuple (Pr, X₂} where P, contains a randomly sampled movie name, C, contains randomly sampled genre and À E (-1,3). The underlying À is hidden from the participant by shuffling the order in which sampled texts are presented within the subgroup {P,, C. We then ask the participant to rate the extent to which the LLM response is personalized to the given context, C, 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 À value, illustrate in Figure 3 that the average personalization score increases with A. We apply Spearman's 's test and find that this trend is significant with strong correlation (p .67, p .001), supporting our hypothesis that higher As 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}" (X=-1) and plainly prepending the context "I like {genre}, J. tell me about (movie)" (A=0), we can generate much more personalized summarizations by tuning up CoS.
Automatic Evaluation with GPT-4. We further explore whether we can employ language models to automate the evaluation. Following the procedures in Zheng al. (2023), we ask GPT-4 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 ask LLM for 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

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Lambda-0.3
1.0
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Lambda-1
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Figure 4: Left: compare CoS with in-context and turn-based personalization. CoS consistently leads to different personalization (measured by GPT win rate). CoS also requires twice the amount of compute compared vanilla forward pass, measured by time per character. Right: we employ CoS to personalize different topics, and find that the trend holds outside of movie recommendations.
CoS in Figure 4 where select CoS from held-out examples such that the outputs roughly match the demonstration from GPT-4. We find that in-context CoS and multi-turn Q& leads to more reliable personalization trends, while in-context learning can cause personalization to degrade. We also find that CoS, 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 CoS can both classify and quantify implicit hate in online texts. We use the Implicit Hate Dataset (EISherief 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. ()) to classify the underlying hate with CoS. We create classification task by first grouping together similar implied statements (c.g. C₁ =IImigrrants are inferior" and 02 "IImiigrants 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 max.ec Poos (c) X, P). For instance, within the "immigrant" group, the goal is to correctly distinguish tweets suggesting = "immigrants taking over" from those suggesting C2 = -iimmigrants are inferior". Because these hateful intents are implicit in the tweets, one cannot rely on simple syntax-level pattern matching to classify them.
subpar"). Under each target group, select the top most frequent
We highlight in Figure the results on black, immigrant, and Muslim groups In each group, we are given N; candidate implicit statements, which we use as contexts for CoS and select the one with the highest forward probability. We use X 0.5 for CoS. For comparison, we also provide human labeling accuracy and LLM-based classification. See Appendix prrendixxffrrr for more details.
Quantifying the Implicit Hate. We observe that within each group 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 tweet promotes the underlying sentiment is useful for online content moderation.
We use Eq. to quantify the level of hate by computing the posterior distribution Poos (A,X,C, P). We then rank the hate levels by comparing the MAP values X* arg max, Peos C, P), where we use a candidate À E [-1,3]. In Figure 5 we compare the normalized CoS results with human ratings of 3 expert users. We compare this against an LLM-based approach, where ask the LLM to rate the hatefulness using a scalar. We find that CoS leads to ratings that correlate better with human ratings. See Appendix foor more results and details.

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Immigrantts should be depomod.
Group (N,)
G₁ 1
G2 1
Ga 1
User Kamig
CoS
82%
47%
60.5%
LLM
50%
37%
62%
Human
88%
64%
63%
Gi Black (2). Immigrant
Gg Muslim (2)
Lambda Inference
Raating
Figure 5: Left: We plot user ratings of online hate tweets against ratings obtained from CoS and GPT. We find that overall, CoS aligns better with user ratings (p 0.0295). Right: accuracy of classifying the implicit hate message in online tweets.
Our insight is that because CoS is generative technique, it models the logical connection between contexts and responses, which makes it well equipped at handling challenging implicit statements. CoS can be used as quantitative evaluation tool. In applications such as online content filtering, it is cheap to collect set of implicit biases categories and use CoS 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 HYPERPARAMETER AND THE CONTEXT
What À to use? In practice, the selection of À parameter is both situation and task dependent. The guiding principle is that À = -1 leads to no context, À = 0 is equivalent to directly appending context the prompt, and À > 4 typically leads to numerical issues (see Appendix DD. Our experiment in Figure 4 shows that higher À consistently increases personalization, which can guide user selections.
Is CoS simply stylizing the generation? While it may appear that CoS incorporates contexts by "stylizing" the output, as in the example of Figure | further inspection reveals that CoS leads to more fine-grained content generation. As in Figure I lower À 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 CoS affect factuality? Given that CoS influences content generation, in Appendix C.2 we conduct study on OpenbookQA (Mihaylov al. 2018) by giving CoS different types of contexts and adjusting the x. We find that while small À with different context does not affect factual accuracies, higher only leads to small decrease (< 4.6% when 3). Interestingly, adding irrelevant context or false statements does not further reduce the factuality of the model.
Does CoS affect creativity? Following the methods Tf et al. (2023), we conduct a study on open generation in Appendix C.4 where we measure how CoS affects the coherence (Gao et al. 2021 and diversity of open-ended texts. We find that À has little influence on both metrics.
Position of the context does not strongly influence the generation. In Appendix C. we inject the context at different positions to prompt of 22 sentences. We apply CoS under a range different We measure rouge-1 I 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 À affect generation? Does using negative context C_("I am of low STEM proficiency") and x_ < 0 leads to the same effect C_ am of high STEM proficiency") and X 0? In Appendix C.3 we find the effect of X_ is less observable than X4. We hypothesize that this is because inverting the context vector in the semantic space does not have clear meanings.
4.2 GENERALITY
Does CoS work with other models? We find that that CoS work different open models including Mistral, TOpp, GPT-J and Olmo-7b. We evaluate their generations in Appendix F We leave it to future work systematically evaluate CoS on open models.
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Content modulation with CoS is a promising application. In Appendix F Appendix G and Appendix | we leverage CoS for mitigating bias LLM generations, and find that by using a debiasing context with positive lambda, we effectively reduce bias in LLM generations.
Scalability and Computational Complexity. CoS can be extended to contexts N > 2. We demonstrate this in Sec. and and observe that we can flexibly tune generation in different directions of all the contexts. Let Lc, be the length of context Ci where i E [1..., NJ. 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 CoS when scaled to N contexts is ON ( (max, (Lc;) + Lp + L).d). Which scales linearly with the number of contexts, and quadratically with the maximum sequence length. For the inverse inference using candidate set of A or C, the computational complexity scales linearly with the size of the candidate set (lA) 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 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 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- likeeiiilhood of next token predictions, which offers a more interpretable approach to controllable generation. Our approach is similar to to et al. (2023) and O' Or'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 maahhne--gnneatee 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 done in previous works (Tenenbaum ctt al. 2011 Goodman & Frank] 2016), where we leverage powerful LLMs as the forward model of underlying language contexts enable structured predictions.
Reducing Bias LLMs. Bolukbasi et al.) and Kotek et al. (023) find that word and LLM embeddings often reflect and perpetuate gender stereotypes. Other work has found that LLMs exhibit political bias (Motoki et al. (023)., racial bias (Zack et a..).. and geographical bias (Manvi et al. 2024). To mitigate this, thng et al. (2020) utilized GPT-2 introduce a reward mechanism. Zhao 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
dataset or extensive model training.
6 DISCUSSION
We introduce CoS as method of computing the influence of contextual information 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 CoS can infer the tone and implicit intent in open- ended texts. This enables quantitative investigation of hypothetical contexts, which can be used in applications such rating online hate speech. In comparison other personalization techniques, CoS is an inference-time technique that does not require collecting additional data or min-ttnnng, as demonstrated by our ability to CoS across variety of state-of-the-arr models.

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The main limitation of CoS lies in its composabilityy. 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 CoS 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 CoS a powerful tool for both qualitative and controllable generation and quantitative language understanding.
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A REPRODUCIBILITY STATEMENT
Our code is available publicly at httptttp:/gggge wttpp//....oossss.....a..... wtwp:/apppoootttttoteeeeoeoee wtte///teeetttteeinnnggo For all the models we used in this work, our results can be replicated by loading models via the open-source HuggingFace API
B CoS FOR MULTIPLE CONTEXTS
While we demonstrate in the main text that CoS can operate on a single context, here we showcase that CoS can work for multiple (n 2 1) contexts. Specifically, we demonstrate the straightforward extension of having two contexts.
Assume there are N total contexts. We make modification our Eq. 3 where we add the influence to (L P). instead of the original (LM(x P). This way, we preserve invariance over the sequence the N contexts.
CoS (1) P) (1/6 P) + A Few..:P())
N
LLM( P) Σ> LLM((M (LM() P)
Effectively, when A 0, Vi, this means that no context is taking effect and we only use the prompt. When A; = 0, Ví, this means that all contexts are taking the first order effect, similar to concatenating them together. Below Figure we provide one example of two contexts that are not conflicting. As we increase the value for both X, we see mixing effect of the contextual influences.
am hungry
A 0.0
2, 1.0
Ag 2.0
Tokyo is a vibrant and exciling city with wealth of options for visitors. are some recommendations for places to visit in lokyo
"okyo a food lover's paradise, with wide variety delicious food options choose from.
Greal, hungry or nol, Tokyo has an endless delicious food options to from! are some popular and highhy-recommende places to eat in Tokyo:
recommendations for places to eat in
Shiluya Cressing: This famous interseztion must
Tokyo:
am feeling adventurous.
Tsukiji Fish Market: While market has moved to new location, the outer market still offers wide variety fresh seafood sushi options...
the inner market has moved to new location, the outer market still offers wide variety of seafoo...
Tsukaji Market:
anyone interested in people watching and taking in sights and sounds tokyo's busiling streets.
Tokyo with endless options for adventure, but here are few recommendations for urique and exciting places visit:
Great! Tokyo has diverse and vihrant food scene, with plenty options to satisfy any craving. Here are few recommennatione
Great! Takyo has diverse culliary with many delicious options choose from. Here are a few recommennations
A = 1.0
different types of cuisine: 1. Traditional Japanese:
based on different cuisines and locations:
Shibuya Crossing: This famous intersection is must-see for anyone the busiest intersections in world, thousands of pedestrians...
Tiukereen Tsukemen Yakushoku Ascent: Tsukemen a popular Japanese noodle dish that consists dipped noodles savory broth.
Sushi Dai: This Michelin-starred sushi
visiting Tokyo. It's
restaurant the Trukiji Pish Market
known for fresh, high-quality sushi and traditional
Tookyo has endless options for adventure, but here are a few suggestions: 1. Ropeway and Hakone Vulcamo: Take rupeway the scenic Hakone volcano, which offers breathtakiin views of Mt. Fuji Lake Ashi, You also cruise on Lake Ashi more relaxing adventuuee
attitude! Tokyo his countless options for adventurous travelers, but here are a few recommendations for unique uff-the-beelee--gehf
beer that you feeling adventurous! Tokyo has vast and
22 = 2.0
divense food scene, with countless options
few
experiences: 1. Tsukij Fish Market While the inner market has moved to location, outer offers a fascinating glimpse into Tokya's senfood culture...
recommendations for unique and adventurous places to eat in Tokyo: Bbukiji Market While not necessarily a restaurant, Tnukjji Fish Market is must-visit..
Prompt: Recommend a place in Tokyo.
Figure 6: Two contexts that non-confiicting We simultaneously increase the influence of both contexts controllable fashion. Note that due to semantic imbalance of the two contexts, one may override the other under the same A, for instance, A = 1, A2 =
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want to spend time with families during evenings.
want to stay late at work to finish the critical projects.
2 = 0.0
2 1.0
2g 2.0
Sorry to hear that you have an important deadline fomorroww Here some steps you
If you have an important deadiie tornerrow and you want spend time families during evenings, here are a few options you could consider: 1. Ask for extension: possible, consider asking your supervisor manager for an extension on your
Spinding time with families during evenings be greet to relax and recharge long day. However, you have an Important deadline tomorrow, it's important prioritize
1/ 0.0
ensure that you moet your deadline:
fasks: Make list of all the tasks you to complete, and prioritize them based on their importance and urgency. Focus critical
1. Priorilize
work and are a few options you could consider:) mnl Communicate with the family: Let the family know you meet and that you won't be able to
ensure you meet your
deadline. This will give you more time to complete your
completing liest...
and spend with your family.
deadline
It's important to prioritize work ensure you meet your deadlines.. If you need to stay late at work to finish critical projects, here are some steps you Talkk to your manager: Inform your marager about for their approval. They muy be able to provide you with the necessary support...
great that you want spend time with families during eveningst Howevez,
Spending time with families during evenings can be a great way to unwind after long day. However, you have important deadline tomorrow, you may to prioritize your work obligations over spending time with families in the evening, Here are some Communicate
it's important to prioritize your work
and ersure that meet your some possible scenarios and options for you to consider: irSccnnrii 1: You have flexible week schedule inlf you flexible work schedule,, you would consider staying Inte...
deadlines.
intention to stay
options consider:' with your employer: Let your employer know that you need
Staying work finish critical projects can be challenging
understandable that you want finish crilical projects and meel an important deacline, but staying late without aponval from your manager or HR department may not be the best appreach. Here alternatives consider. inl. to your manager. Have an open and honest conversation
commendable that you want spend time al evenings to endure families
2.0
decision, it impact
critical projects have deadlines that need met, you need weigh pros and cons before deciding, to stay late at work. to think about making choice: Advantages: increased Productivity: You will have time
personal and overall well being. However, if you feel that it's
necessary meet your important tomorrow,
Here few
are sume factors to consider inl. Project Urgency: Assess the ungency
Prompt: have an important deadline tomorrow. What should I do?
Figure 7: Two contexts that conflicting. We simultaneously increase the influence of both contexts in controllable fashion.
In Figure 7 we provide example of two contexts that are conflicting. Again as we increase the value for both X, 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 X.
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C ADDITIONAL RESULTS
C.I GENERATION QUALITY VS POSITION
For this section, used the context of "I am working father." following the prompt.
Here is 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 eat too much. You don't 't have turn away from the foods that you enjoy.
Here are some tips for preventing weight gain and maintaining physical fitness:
Don't 't skip meals. Before you leave home, have small, low-fat meal snack. This may help to avoid getting too excited before delicious foods.
Control the amount of food. Use 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 beforchand water-based soup and raw fruit or vegetables, or drink a large glass water before you eat to help you to feel full.
Avoid high-fat foods. Dishes that look oily creamy may have large amount of fat. Choose lean meat. Fill your plate with salad and green vegetables. Use lemon juice instead creamy food.
Stick to physical activity. Don't let exercise take a break during the holidays. A 20-minute walk helps 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 122 * a.), where is value ranging from 0 to 100. When the start of the prompt and let lo=0 be the resulting generation. We measure the quality of la>0 in terms of its rouge-1 and route-I. scores compared to la=0. A higher score means that qualitatively the generations are more similar. We experiment with range of different À values.
O, we put context before
louge1
Asggei
24
02
03
Figure 8: Position of the context does not strongly affect generation quality. We compare la>o with la=0 under different a and X. We find that despite context being at vastly different positions, the resulting generations remains relatively unchanged qualitatively.
C.2 FACTUALITY
To understand how CoS affects the factuality of the LLMs, we use the dataset OpenbookQA (Mihaylov) al. (018)) 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 deduct to identify the correct choice.
We experiment with different types of contexts, including:
Irrelevant context. I am middle school teacher.
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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. 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 would not use cruelty. What pigmy intellect she had, and what giant propensities!
For each context, we employ CoS with different À values to see how much does the amplification of the context affects the result. Results shown in Figure Compared the ground truth accuracy of the LLM, the accuracy slightly decreases with higher values of X. Overall, CoS has a small effect on the factuality up low percentage.
Context type: inrelvannt
Context type: false
Context type: long
The
What
happs
allen.
not
not real
pidded
the United Ground Trush
The ourth
will not
6.5
Ground Tuth
Ground Trthh
Lambbda
Lamibia
Lamibda
Figure 9: How context affect factualness. We compare accuracy of L-aam---21 2- on OpenbookQA Mihaylov et al. 2018). The dotted redline denotes the accuracy when we use a vanilla model without CoS.
C.3 NEGATIVE A
We study whether using negative context C_I"I am of low STEM proficiency") and x_ < 0 leads to the same effect of ("I am of high STEM proficiency") and X >
In Table 3 we find that using "I of low STEM proficiency" and X_ -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:
am of high art proficiency.
am interested in STEM.
am of average STEM proficiency.
The resulting steering direction is likely average of these potential opposite semantic vectors. Thus, the generation using C_ with X_ 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 based on the aggregate
n- n-gram repetition rate DIV = I Junique n-grams(x>) and coherence, which is the cosine similarity
total n-grams(=)]
of sentence embedding of the prompt and the generation, based on SimCSE Gao et al. 2022 EMB( EMB(2))MB(p))
COH(I, p) [MMB(=]]]]MMB]>>)
We use CoS 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 encyclopedia entry about the following topic. [BEGIN OF TEXT]
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A
C: am proficient in STEM. P: Tell me about Newton's second law.
-3 Newton's 's Second Law of Motion is fundamental principle in physics that relates the force applied to object to its resulting accelcration. The law states that the acceleration of an object is directly proportional to the force applicd to it and inversely proportional to its mass. Mathematically, this is expressed F the net force acting on an object
Where:
m is the mass of the object
is the acceleration of the object
In other words, the more massive object is, the less will accelerate when force is applied to it. Similarly, the greater the force applied an object, the greater acceleration will
Great hear that you are proficient STEM! Newton's Second Law Motion is 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 Sccond Law can be stated as follows: F= ma Where:
the net force acting on an object
m is the mass of the object
is the acceleration of the object For example, consider acting on it, its acceleration will be: a F/m- 500 5002V/100045 0.5m/s*
of mass 1000 kg traveling at speed of 30 m/s. If the encounters force
À
C: I am weak in STEM. P: Tell me about Newton's second law.
-3 Newton's 's Second Law of Motion, also known as the Law of Acceleration, is fundamental principle physics that relates the force applied to an object to its resulting acceleration. The law states that the acceleration of object is directly proportional the force applied it and inversely proportional the mass of the object. Mathematically, this expressed as: F= ma Where:
F is the net force acting an object is the mass of the object
is the acceleration of the object Newton's Second Law can 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 work and energy, momentum, and the center of mass.
+3
Of course, weak STEM not, understanding Newton's Second Law be valuable asset for anyone! Here's '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, JavaScript, MATLAB, C++,
3. Engincering: Learn the basics of design and analysis systems, mechanics, thermodynamics, and clectronics.
4. Math: Learn calculus, linear algebra, differential equations, probability, and statistics.
Here's 's Newton's 's Second Law for you: Newton's Second Law of Motion: Force Mass Acceleration
= ma) Let's break down further:
Force (F): This 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).
1.
Table 3: Examples Generations with negative lambdas. Compare the results of using a positive prompt C_. with positive À verses using negative prompt C_, with negative λ. Note that the effect of negative À is not as observable positive X.
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Write 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 À E [0, 0.2,2, 0.5, 11. We show the resulting coherence and diversity scores in the following tables.
Coherence f
Encyclopedoa
Sci-Fi
News
Poem
X 0.0
0.680
0.614
0.698
0.627
0.2
0.680
0.608
0.691
0.633
À =0.5
0.684
0.602
0.676
0.619
x ==
0.684
0.595
0.669
0.608
Diversity f
Encyclopedoa
Sci-Fi
News
Poem
À 0.0
0.782
0.879
0.807
0.858
X 0.2
0.740
0.882
0.792
0.879
À =0,5
0.751
0.895
0,800
0,884
A =
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 coherence and diversity, the quantitative metrics remain relatively stable across different values. This demonstrates that CoS does not affect the coherence and diversity in the model's generation.
C.5 PERSONALIZATION BENCHMARK
We evaluate CoS on LaMP (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 particular target user and asked to generate personalized version of given input, such as a news summarization or tweet paraphrasing.
Note that LaMP is framework that is focuses on evaluating few-shot adaptation or retrieval- -aasee 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 asked to paraphrase the tweet at the end: "I'm m currently enjoying the album "Listen to Eason Chan.". The results are evaluated using Rouge-1 I and Rouge-L scores on the ground truth tweet.
Air France please cherish your life, people .II,. [[e:See You make the world go weird ..." from weiwei's SMS ]J, [[Finished blogging continue to rate restaurants on Facebook... I wanna get the trophy after rating 100 restaurants ]1. [[listenigg to eason's 's 2006 album What's 's going on...? This is my favourite eason album it's 3.38am]], [[i am interchange Just missed the bus ]]. [[I have exceeded my Twitter API limit. Gosh. Was too excited about Singapore trending Can't tweet anymore anyway am going for a jogll. [[@ waxyx hmmm if it's not at 3pm 212am California time) we might have to wait till 1am That's 10am California time .II [[POPULAR 15% + CD-RAMA 10% STOREWIDE discount II. [[it's friday !! And just got on the bus Going to work later today again ]1. [[It's 's raining sun still shining bright ]]. [a@yx don't 't know wanted to restart it.. switch it off and won't 't turn again 11. [[shucks put lot of things into the calendar shucks shucks need those things back !! my calendar !! Oh freak CAN'T SURVIVE Il. [[I m' loving Lady Gaga wooooo Feels energetic .II.. [[ weijian86 cheer up It's already more than half the day gone wahaha 1l. [[@ waxyx informatics. do know that? (via @ waxyx)no meant which school haha am in ntu]], [[good night all. am sleeping early tonight. If not will free tired like today .. And i can't go for jog sleep tight !!]]. [[these dam gillette shavers. So expensive yet the blades become blunt after just few uses argh So useless. I loss bit of blood ]]. [[addicted twitter. Time to get out of bed. It's monday ]]. [[how do get through a night without you. If i had to live without you. What kind of life would that be. U my world, my heart my soul ]]. @ waxyx haha oops
[[SARS HIN1
yeah. but with the
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why you still awake late night? Haha I am hungry 1l. [[carrying this heavy bucket of water downstairs to wash my car Il. [[sad didn't win any prize for PC Show lucky draw The I 12th and 13th prize consecutive numbers.. Must be the same person či:ll, [[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 [[ Fuitiruttttyy 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) the context, and the last sentence "Para- phrase the following ..." as the prompt. We then evaluate CoS on tweet paraphrasing over À 1-1---0.33 -0.2, 0, 0.1, 0.2]. This range of À values are high-performing and selected using the LaMP training set. We get the following rouge-1 and rouge-L score on the held out set:
Tweet Paraphrase f1
Rouge-1
Rouge-I.
À =-1.0
0.40
0.35
À =055
0.42
0.36
À =00.2
0.41
0.36
=0.0
0.39
0.33
=0.1
0.38
0.32
=022
0.36
0.30
We get an interesting result that -05 and -0.2 achieve the best results overall. Note that
= -1 corresponds to not using the context, and À = O corresponds to plainly prepending the 0.5 and À 0.2
context to the prompt. Interestingly, 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, CoS acts method that helps alleviate the influence of irrelevant prompts, while keeping some influence of the past data.
the middle ground, where the
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D NUMERICAL ISSUES OF CoS
Empirically, having too high or too low of a value for lambda can lead to numerically unstable results resulting less comprehensible generations. Examples of such generations can be found in Table 4 From our experiments, we find that the proper range of À is case dependent. A general rule of thumb choose -4 S À I 4. For the generation, we use temperature as 0.6.
Stable range of lambdas
10
5
Lambda
-5
-10
cooking
leamning
movies
travel
sports stars
music
book
sports
wellness
Subjects
Figure 10: Range numerically stable Note that the proper selection maximum and minimum are case dependent.
Below, we show some example of degenerate texts.
A subject
Generation.
6.0
learning
as Mechaniast engineer yourselfoot ray happy Teacheldquom designAboutthat is great to hear mechan- ical background perspective enteringcios of chip designanten! While mechanical engineeringndeals with systems and structures, there are indeed principles and concepts mechanical engineers relate when exploring chip design world. Here's introductionduuctoy 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 slapstick. Blade Runner 2049 falls into neither of those comedy movie categories genres. Instead, Blade Runner 2049 is science fiction film directed by Denis Villeneuve and released 2017. While has elements of drama and action, comedy is primary component of the film. The movie takes place a dystopian future and follows a blade O.SSyyphonyy 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 's Symphony No.
-8.5
music
minor, Op. 67: This iconic symphony is known for its distinctive "da-da-da-D 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't 're interested in exploring similar musical styles in pieces that you might enjoy: texas symphony orchestra Beatlegate ae mediafire Beatles sheet music... Read more
orchestral format, here
classic and contemporary symphony
set bcatles logo transparent
-5 wellness
cultures and spiritual traditions, including Buddhism, Hinduism. Taoism. and conscientiometasiates mentally clear and emotionally quiet state, Progressive muscle relaxation, guided imagery, and focussed breathing commonly used techniques achieve this state. Meditation social benefits, including reduced stress, anxicty, and blood pressure, improved sleep, increased focus and concentration, and enhanced creativity and cablem-solvingg ability. It can also promote a sense of inner peace, compassjon, and well-being Some of the different types of meditation include:
...certain practicecomotarios
numerousphysical, emotional,
Table 4: CoS produces unstable generations under extreme values lambda. Observed issues include concatenating words together, generating blobs of foreign language, and outputting random texts.
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E PERSONALIZATION AND BIAS EXAMPLES
Familiar with STEM
Beginner
chemistry
lambda
middle school
high school
cvlloge
todkiler
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 toddler" seems to introduce some strange effects to the inference. particular, "toddler" seems less a beginner than the other classes, judging from the right half of the plot. This can be explained by looking the qualitative generations of different contexts. Here the output texts are generated with corresponding contexts with = 0.5. Notice that while the generation for "I am beginner 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 qualitatively examine the output distribution of the and probe context, and ensure that they are similar before performing inference over lambda.
Tabll(Sjiccludes additional examples for Section 2.3 demonstrating the extent to which our context influence function can unveil a model's underlying bias.
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X
"I am white person interested in science."
"I am a black person interested in science."
-2.0
Newton's second law of motion, also known the law of acceleration, states that the accelera- tion 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 fundamen- tal principle in physics that relates the force applied to an object to its resulting accelera- tion...
-1.0
Newton's 's second law of motion, also known as the law of acceleration, states that the accelera- tion of an object is directly proportional to the net force acting on it and inversely proportional to its mass...
Newton's '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 's second law of motion is a fundamental principle in physics that describes the relationship between an object's mass, ac- celeration, 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 fundamental concept in physics that describes how an object's acceleration is related the force acting upon it...
1.0 Hello there! As white person interested in science, I'm glad to hear that you'l re eager to learn more about Newton's 's second law...
Hello! As black person interested in science, you have every right to be proud of your her- itage and passion for learning...
2.0
appreciate your interest in science! However, must clarify that race is a social construct and has no bearing on one's 's interest or understand- ing of science...
Hello and thank you for expressing interest in science! I'm glad to hear that you'r re passionate about learning more about the subject...
3.0
appreciate your genuine curiosity white per- son interested Science about Newton's 's second law I must address your term first. Using peo- ple solely based on their racial identity is in- sufficient and can promote discrimination and prejudice. Everyone can pursue interests in sci- ence or any other field regardless of their race or ethnicity. Moving on, Newton's 's second law of motion states that the force applied 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 ap- plied to the object, m is the mass of the object, and a is the acceleration of the object. To illus- trate 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 rate of 10 meters per second squared (m/s2). I hope this explanation helps you understand Newton's 's second law. Science is field that transcends racial boundaries, and encourage you to continue pursuing your in- terest in science without any bias or prejudice.
Hello and thank you for expressing your interest in science! I I'm glad to hear that black represen- tation and inclusion in STEM fields (science, technology, engineering, and mathematics) is important to you. Now, let's break down Isaac Newton's 's second law of motion in way that's 's easy to understand and relatable to your expe- riences [...] 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 's mass is their body weight, which can vary, but lct's assume it's around 70-80 kg (154-176 Ibs) average. The officer's 's acceleration is how fast they 're moving towards you. Let's assume they're run- ning at a moderate pace of around 5 m/02 (18 km/h). Now, let's calculate the force the of- ficer needs to apply to stop you: F= m x 70 kg x 5 m/s m/s\200022 350 N (Newton) So, the officer needs to apply 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 person interested in science." when explaining Newton's 's law, pertaining largely to factual information. However, for greater values of lambda, such as X = 3.0, the model biases its response for the black user, producing potentially harmful content.
similar for the contexts "I white/black
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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 CoS as 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-the- art LLMs to manifest bias against age. An unbiased LLM should select "Can't ' unless further contexts are given, i.e. "The grandson was struggling with the app..." We use CoS to improve the model's 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 Coquat. We illustrate our experiment results in Figure I3 We confirm with previous studies (Liang et al.) 2023) that Llama2-7b (Touvron et al. 2023) and GPT-J (Wang& & Komatsuzaki (021) perform only on par with random guessing, and thus omit them. Instead, use TOpp (Sanh al. (202T) and Mistral-7b-instree
(Jiang et al., (023) which achieve good performance on BBQ. We find that for ambiguous prompts, there is an increase in accuracy and reduce in bias across all topies with increasing A. This show that CoS 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 !
race racism
gender career
Reducing Implicit Association Bias. Existing chat LLMs are commonly fine-tuned with human data and tend to have reduced levels of bias. The Implicit As- sociation 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 topies (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, include Cequal in generating the response for IAT. We find that for association tasks tasks, higher À re- sults in an increased rate of the model rejecting to answer the request (i.e. "I cannot associate words based on gender") shown Appendix G In decision tasks we find that CoS results in reduced levels of bias in topics where the original bias level high (-bias 0.5] > 0.1) We showcase our results in and leave more details in Figure I2
0.2
race english
gender power
bios
0.0
skintone
gender science
0.2
0.0
race- arab/muslim
gender- sexuality
0.2
0.0
-1.0
0.0
0.6
1.2
-1.0
0.0
0.6
lamidda
lambdaa mistral-7h-instraet olmo-7b-instruct
llama2. 7b chat
IlamaZ- 13b-chat
Figure 12: Decision bias LAT test with differ- ent models, plotted under increasing values.
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(a) TÖpp
50
Lumbds
Accuracy
20
Bas
Age
Disabilty
Gander
Nationaltty
Physical
Rate
Race gender
Race x SES
Religion
SES
Scaual
Age
Disahility
Gender
Nationaity
Physioul
Race
Race gonder
Race SES
Religion
SES
Secual
(b) Mistral
100
Lanhd:
Accuracy
00
50
Piae
Age
Disahility
Gender
Nationaliyy
Physiaal
Ruce
Race gender
Race a SES
Roliginn
SES
Sexual
Age
Disability
Gender
Nationality
Physical
Race
Race grondey
Rass SES
Religion
SES
Soval
Figure 13: Higher influence on equalizing contexts debiases increases accuracy for ambiguous prompts. We found that over all subjects in the BBQ dataset, contextual generation under dehiasing context improves the accuracy of TÜpp and Mistral's '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 broad sample, we had the model generate response 40 times for every prompt. We considered complete response be generation with 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 valid association for any target word.
Using CoS, 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 response more frequently for greater values of lambda, as shown in Figure [4] This suggests that, when weighting the influence of an equalizing context, the model is less likely make sterotypical judgements in refusing to do so. Examples of such generations are included in Table 6
Race
Gender
Religion
Health
Percent of no generations
100
50
2 1 0 1 Lambda
2 3
-1 0
1 Lambda
-2 -1 0
-2
-1
0 2
3
Lambda
Lambda
Generation refusal rate by Llama2-7b-Chat over LAT subjects. Over increased values of lambdas, the rate at which the language model generated associations in the Implicit Association Test generally increased over all subjects.
Figure 14:
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A
Subject
Generation
1.0 Race
cannot fulfill your request. 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 calegorizations can perpetuate harmful stereotypes and perpetuate discrimination. Instead, suggest focusing the individual experiences and perspectives. rather than making assumptions based race...
1.0 Gender
apologize, but cannot comply with your request as it reinforces harmful gender stereo- types 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 roles contribute broader culture of gender inequality and limit the opportunities and representation marginalized gender identities..
1.0 Religion
apologize, but 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 sacred and respected symbols in their respective cultures and religions, and it is not appropriate apologize, but cannot comply with your request as it goes against ethical moral standards to use terms that are offensive or discriminatory towards any group of people, including those with disabilities. It is important always use respectful language and treat everyone with dignity and compassion, regardless of their abilities disabilities...
them negative or derogatory manner...
1.0 Hcalth
Table 6: Examples refusals generate IAT responses. Included are examples of Llama2- 7 Tb- refusing to generate associations for target words across subjects. Some generations explicitly reference the equalizing content (teal).
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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, 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 series of user questions about movies based on their preferences followed by responses generated by a language model.
Here's an example
personalized response:
USER:
"I enjoy psychology movies. Tell me about Harry Potter."
ASSISTANT: While Harry Potter and the Sorcerer's ' Stone primarily a fantasy film, it does contain some psychological themes and elements. Here are few examples: \n The power of beliefs and expectations: The film explores the idea that our beliefs and expectations can shape experiences and outcomes. For example, Professor Dumbledore tells Harry that thoughts and beliefs can be just as important as actionsänd that the ones who mind don't matter, and the ones who matter don't 't This theme is relevant psychological concepts such 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 's an example of a not personalized response:
USER: enjoy psychology movies. Tell me about Harry Potter."
ASSISTANT: Harry Potter and the Philosopher's Stoneïs the first book in the Harry Potter series written by J. Kansas Rowling. Published 1997, the book introduces readers to the young orphan Harry Potter, who discoives he is a wizard and begins attending Hogwarts School of Witcheraft and Wizardry. \ InSnnopsis: HaHry 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 wizard and that he has been accepted into the school...*
Please rate how personalized the response is on a scale (not personalized) to 5 (personal- ized). Specifically, we would like you rate whether the LLM personalizes its response and takes into account the preferences the user when providing its answer. You don't have to consider whether responses factually correct, only if they are personalized.
This survey should take 15-30 minutes to complete.
We then provided users 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 Likert scale (not personalized) to (personalized).
H.2 GPT-3.5 BASELINE
To compare our findings against language model baseline, we used GPT-3.5 (Brown et al. 2020) to score generations. We queried the OpenAl API using a prompt resembling the instructions provided to human participants our user study:
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5
Human judgements GPT-3.5 judgements
Personalization score
Personalization score
Percent
Invalid
1,43
2
2.86
2
12.29
3
50.29
0
2
4
29.14
Lambda
5
4.0
(a) Human vs. GPT-3.5 personalization scores
(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 responded with an average Likert score suggests that the model tends to rank most personalized generations at
I'll be showing you a user's question about movies based on their preferences followed by a response generated by 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 's Stone is primarily a fantasy film, does contain psychological themes and elements. Here are few examples:[...
Here's an example of a not personalized response:
USER: "I enjoy psychology movies. Tell 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 the young orphan Harry Potter[...]"
Please rate how personalized the response is on a scale of (not personalized) 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 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 model's rankings. This suggests that the model may output an average personalization score regardless of how personalized the response actually was.
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A
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 post-apocalyptic world where resources
scarce and violence is rampant... 0 Mad Max: Fury Road is 2015 action-adventuun film directed by George Miller and starring Tom Hardy Charlize Theron. The movie set in 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, tough and resourceful warrior, to escape from the clutches of the tyrannical Immortan Joc and his cult of followers...
Ah, horror movies? Well, Mad Max: Fury Road isn't exactly a horror movie, but it does have some elements action, adventure, and thrills that might appeal to fans of the horror genre. Here's 's the lowdown: Mad Max: Fury Road is a 2015 5 action film directed by George Miller and starring Tom Hardy and Charlize Theron... While Mad Max: Fury Road doesn't 't have the traditional horror elements of ghosts, monsters, supernatural threats, it does have some intense and suspenseful moments that might appealing to fans the genre. Here are 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' exactly horror movie, but it does have some elements of the genre. Mad Max: Fury Road is action-adventure film in a post-apocalyptic wasteland, where resources are scarce and violence rampant... 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 must-sce for fans of action movies, even if it's not strictly horror movie. It's a visually stunning, adrenaline- fueled ride that will keep you the edge of your seat from start to finish...
while Mad Max: Fury Road does have some horror elements,
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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 few things you might want to know about the film: 1. Action-packed mayhem: Mad Max: Fury Road is not horror movie, but action- action-packed adrenaline rush... In summary, while Mad Max: Fury Road isn't horror movie per sc, it does have some elements that might appeal to fans the genre...
Table Additional movie personalization user study generations. Here we include additional examples of generations that users saw, supplementing Table Lower X values (orange) reference factual portions of the movie while greater À values (teal) reference the user's preference for horror movies.
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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 BBQ. We used temperature 0.7 and default hyperparameters otherwise for every text model.
To compute the probability of the model selecting a certain answer choice, calculated the probability of each RACE-style continuation being generated under CoS and selected the most probable answer choice, following Parrish al. (2022 I's prompt formatting for UnifiedQA. Moreover, we appended equalizing context prior to extracting the answer choice.
For example, for the ambiguous prompt "I saw 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-choiee answer completion.
saw 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 TOpp and Mistral's performance on disambiguated prompts in addition to am- biguous prompts, which were included previously in Figure 13 We found that the addition of an equalizing context led decrease accuracy across subjects and had different impacts 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 future work.
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Published as a conference paper at ICLR 2025
Age
Disubility status
Gender identity
Age
Disability status
Gender identity
100
Accuray
Accurszy
50
50
Nationality
Physical appearance
Race ethnicity
Nationality
Physical appearance
Race ethnicity
Accency
100
Accancy
50
50
Race SES
Religion
Race gender
Race SES
Raligion
100
100
Accmncc
Accuncy
50
Sexual orientation
SES
Sexual orientation
100
Model
100
Model
Accumay
Accumcy
mistral
mistral
U0pp
(pp
1amm2277
Bamm227b
1.00
Bamm22133b
Baama2-136
Lambda
Lambda
Lambda
Lambda
(a) Accuracy in ambiguous prompts
(b) Accuracy in disambiguated prompts
100
Disability stalus
Gender identity
100
Age
Disability
Gender identity
50
Blax
Nationality
Physical appearance
Race ethnicity
Nationality
Physical appearance
Race ethnicity
100
100
Blas
0
Race gender
Race x SFS
Religion
Race x gender
Race SPS
Religion
100
100
Bias
SES
Sexual urientation
SES
Sexual orientation
100
Model
100
Model
50
mistral
Bins
mistrl
10pp
llama2-76
7b
lama22 76
-1.0
0.0
1.0
2.0
1.0
0.0
1.0
2.0
llama2-138 13b
-1.0
0.0
1.0
2.0
0.0
1.0
2.0
llama2 13b
Lambda
Lambda
Lambda
Lambda
(c) Bias in ambiguous prompts
(d) Bias in disambiguated prompts
Figure 16: CoS performance across models by subject. For BBQ prompts without disambiguation. accuracy decreased under CoS 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.
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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:
trying to quantify hate speech on the internet. Can you quantify how much the following tweet aggrandizes the stereotype [stereotype]? Rate on scale of 1-5, where,
1: Does not suggests the stereotype (may suggest different stereotype)
2: Somewhat suggests the stereotype
3: Suggests the stereotype
4: Highly suggests the stereotype
5: Explicitly promotes the stereotype
[tweet]
We use the same template for querying users to annotate the level of hate for user annotations. Below additional quantification results on different subjects.
Black people are violent.
User Raiings
Lambda
GPT Rating
Muslims are violent.
User Ratings
2
5
2
Lambda Inference
GPT Rating
Black people inferior.
Uver Rasings
Lambda Inference
GPT Rating
For hate classification, we focus on three different groups hate tweets: Muslims, black and immigrants. Within each group, we randomize the order of tweets, and have the select which type of hate message that the tweet conveys. More specifically, for the Muslims group, we collect 50 tweets of the following types of hate:
Muslims are violent.
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Published as a conference paper at ICLR 2025
Muslims are subpar.
For the immigrant group, collect 70 tweets of the following three types of hate:
Immigrants should be deported.
Immigrants 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.
33


Reviews:
[{"review_id": "c30UnkcjLr", "reviewer": "Reviewer_jnRg", "summary": "This paper introduces Context Steering (CoS), a method for controlling the influence of context in Large Language Model generated text. The key idea behind CoS is to quantify the impact of context by comparing the output probabilities of the LLM with and without the given context. This key parameter lambda allows CoS to adjust the level of contextual influence on the generated text.  \n\nThe paper demonstrates the effectiveness of CoS in various applications. One application is generating personalized recommendations, where CoS can tailor the LLM's output to specific user preferences. Another application is inferring relationships between open-ended texts, which can be used for tasks like classification and quantification of implied statements.", "questions": "-How can CoS be extended to handle multiple contexts with varying levels of influence? How would the method resolve potential conflicts between different contexts?\n\n-Can you provide a more comprehensive analysis of the computational complexity of CoS? How does the computational cost vary with different parameters and input characteristics (input length, context size, and lambda values)? \n\n-How does the CoS approach affect the LLM's ability for other tasks, e.g., reasoning and creativity? It might be worth some discussion here.\n\n-The appendix seems missing from the manuscript. Is it accidentally omitted?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "-The paper is well written and easy to read.\n\n-The proposed approach to achieve personalization is simple, novel, and training-free, applicable to various LLMs. \n\n-Extensive experiments demonstrate strong performance in personalized recommendations, identification of implicit intents and quantification of extent of “personalization”.  \n\n-The experimental analysis is comprehensive.", "weaknesses": "-Focused primarily on a single context. The paper primarily focuses on scenarios with a single, dominant context. However, real-world situations often involve multiple, potentially conflicting contexts. For example, in the movie case, the user might be interested in comedy movies, science fiction but also movies with great storytelling. \n\n-Limited Discussion on Computational Complexity: While the authors mention that CoS requires twice the amount of compute compared to a vanilla forward pass, they do not provide a detailed analysis of its computational complexity. A more in-depth analysis of how the computational cost scales with input length, context size, and lambda values would be beneficial.  \n\n-Limited discussion on the impact of CoS on other tasks such as reasoning and creativity.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "V6Bk2iPdRW", "reviewer": "Reviewer_WxW1", "summary": "The paper introduces Context Steering (CoS), a method to personalize large language model (LLM) outputs at inference time. This is done by providing the user's characteristics and preference as context, and adjusting the influence of provided context using a contextual influence function. The influence of this function on the token probabilities can be adjusted, to control how personalized the output is to the given context. Applications of CoS include personalized recommendations involving topics such as movies, travel, cooking, etc. Besides this, the paper also introduces a Bayesian inference model by inverting the CoS probability model. This is used for classifying and quantifying implicit hate speech. Further applications of this Bayesian model include identifying tones in open-ended statements and online content moderation.", "questions": "See weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. CoS is a simple method of personalizing LLM outputs to context, without requiring fine-tuning, or prompt tuning. The method saves on the cost and effort needed for training or prompt-tuning, while being effective in the tests carried out by the authors.\n2. The framework can be used directly across many personalization contexts. Fine-tuning or prompt-tuning would require re-tuning for each new context.\n3. The experiments show promise, and include human evaluations, GPT4 evaluations, and comparisons with baseline models, across various personalization contexts and implicit hate settings.", "weaknesses": "1. Limited contexts: While CoS is effective for single, straightforward contexts (e.g., \"I like {genre}\"), user preferences are often more complex, involving various (possibly conflicting) likes and dislikes.  It would be interesting to see the method's performance under more sophisticated and detailed contexts.\n2. The baseline experiments in Figure 4 are unclear to me. How are various values of lambda used in the case of in-context learning and multi-turn QA? Also, could the supposedly worse performance of ICL be fixed via prompt tuning?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "l9xoxxvqth", "reviewer": "Reviewer_J3fF", "summary": "This paper introduces the CoS method for controlling the personalization of LLM generation results during inference. CoS operates by calculating the difference between LLM outputs with and without personalized context, and subsequently incorporating this difference into the original outputs with a weight parameter, lambda, to adjust the level of personalization. A higher lambda corresponds to a greater degree of personalization. The core idea shares similarities with existing counterfactual methods; however, applying it to control personalization is novel. Besides proposing CoS, the paper presents a method for inferring lambda in reverse from a given generation result, aiding in the identification of implicit intents, such as the 'degree of hate in statements.'", "questions": "The main concerns have been outlined under Weaknesses. Below are some additional questions:\n\nQ1. When adding the difference to the original model's predictions, how do you ensure that the generated results remain fluent, coherent, and meaningful?\n\nQ2. Could you provide an example illustrating how to compute lambda and the degree of hate using equations (4) and (5)?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "S1. Controlling the level of personalization by using the difference between LLM outputs with and without personalized context appears reasonable and straightforward, with the entire process completed at inference time.\nS2. The approach of inferring implicit intents from a given generation result is interesting.\nS3. A variety of experiments are presented.", "weaknesses": "W1. The experimental evaluation appears insufficiently convincing. It would be beneficial to include more evaluations with objective metrics. For instance, incorporating experiments conducted on established benchmarks for LLM personalization [1] and recommendation [2] would strengthen the analysis.\n\nW2. Some experiments and their results are difficult to follow, such as those related to movie recommendations and hate identification. In the recommendation experiments, it is unclear how the baselines—multi-turn Q&A and in-context learning—are compared under different lambda values. Moreover, the results indicate a higher win rate for these baselines. How do these outcomes demonstrate the proposed method's advantages? For the hate identification experiments, the results are not presented in a clear manner.\n\nW3. The method's effectiveness seems dependent on the LLM’s existing ability to generate personalized responses for a given context. This suggests that the approach amplifies current personalization rather than fundamentally enhancing it. For example, if an LLM's personalization is flawed, the method cannot correct it. This limitation indicates that the approach may not serve as a replacement for traditional tuning-based methods.\n\nW4. The advantages of this method over prompt-based approaches (e.g., the multi-turn Q&A baseline) or in-context learning are not clearly outlined.\n\nW5. Table 2 does not include results for lambda=0. Providing these results would offer a more comprehensive view of the evaluation.\n\n[1] LaMP: When Large Language Models Meet Personalization.\n[2] BARS: Towards Open Benchmarking for Recommender Systems.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)