Impact of LLM Alignment on Impression Formation in Social Interactions
Authors: Ala N. Tak
Anahita Bolourani
Daniel B. Shank
Jonathan Gratch
Venue: COLM 2025 (2025)
Keywords: Large Language Models
Impression Formation
Alignment
Preference-tuning
Affect Control Theory
TL;DR: Tests of LLMs against Affect Control Theory in gendered social interactions reveal that alignment influences impression formation unpredictably and that models largely ignore context in favor of the actor's identity.

Abstract: Impression formation plays a crucial role in shaping social life, influencing behaviors, attitudes, and interactions across different contexts. Affect Control Theory (ACT) offers a well-established, empirically grounded model of how people form impressions and evaluate social interactions. We investigate whether Large Language Models (LLMs) exhibit patterns of impression formation that align with ACT's predictions. As a case study, we focus on gendered social interactions—how an LLM perceives gender in a prototypic social interaction. We compare several preference-tuned derivatives of LLaMA-3 model family (including LLaMA-Instruct, Tulu-3, and DeepSeek-R1-Distill) with GPT-4 as a baseline, examining the extent to which alignment or preference tuning influences the models' tendencies in forming gender impressions. We find that LLMs form impressions quite differently than ACT. Notably, LLMs are insensitive to situational context: the impression of an interaction is overwhelmingly driven by the identity of the actor, regardless of the actor’s actions or the recipient of those actions. This stands in contrast to ACT’s interaction-based reasoning, which accounts for the interplay of identities, behaviors, and recipients. We further find that preference tuning often amplifies or skews certain impressions in unpredicted ways. Our corpus offers a benchmark for assessing LLMs' social intelligence; we encourage further research using ACT-like frameworks to explore how tuning influences impression formation across diverse social dimensions.

Published as a conference paper at COLM 2025
Impact of LLM Alignment on Impression Formation in Social Interactions
Ala N. Tak
Anahita Bolourani
Daniel B. Shank
Jonathan Gratch
Institute for Creative Technologies, University of Southern California
? Department of Statistics and Data Science, University of California, Los Angeles Psychological Science, Missouri University of Science and Technology antak@ict.usc.edu anabolourani@ucla.ddi shankd@mst.edu gratch@ict.ss.edd
Abstract
Impression formation plays crucial role in shaping social life, influencing behaviors, attitudes, and interactions across different contexts. Affect Con- trol Theory (ACT) offers well-established, empirically grounded model of how people form impressions and evaluate social interactions. We investigate whether Large Language Models (LLMs) exhibit patterns of impression formation that align with ACT's predictions. As a case study, an LLM perceives gender
we focus on gendered social interactions- in prototypic social interaction. We compare several preference-tuned derivatives of LLaMA-3 model family (including LLaMA-Instruct, Tulu-3, and DeepSeek-R1-Distill) with GPT-4 as baseline, examining the extent to which aligmment or preference tuning influences the models' tenden- cies in forming gender impressions. We find that LLMs form impressions quite differently than ACT. Notably, LLMs are insensitive to situational context: the impression of interaction is overwhelmingly driven by the identity of the actor, regardless of the actor's 's actions or the recipient of those actions. This stands in contrast to ACI's interaction-based reasoning, which accounts for the interplay of identities, behaviors, and recipients. We further find that preference tuning often amplifies or skews certain impres- sions in unpredicted ways. Our corpus offers a benchmark for assessing LLMs' social intelligence; we encourage further research using ACT-like frameworks to explore how tuning influences impression formation across diverse social dimensions.
1 Introduction
Warning: Contains explicit or potentially upsetting language.
To successfully navigate the social world, people must quickly assess others' traits, inten- tions, and social roles. This process-known as impression cognitive and affective judgments people make about others based on their social roles and actions (Smith-Lovin & Heise, 1988; Reeder et al., 2004; Rogers, 2018). Understanding impression formation is crucial in many domains. For instance, it explains how gendered beliefs shape salaries (Freeland & Harnois, 2020), how lawyers are perceived their daily practices (Fields, 2023), and how perceptions of criminal behavior align with crime rates (Boyle, 2024). It also aids in understanding cross-cultural differences related to rigidity in cultural norms (Zhao, 2023), investment behaviors shaped by self-perceptions (Charness & Gneezy, 2012; Gurieva et al., 2022), and how social norms can affect leadership roles (Heise et al., 2019; Stewart et al., 2021). Notably, impressions extend beyond isolated entities to the actions people perform and the motivations behind those actions. For example, when learning that someone hit child, observers form expectations about the person's identity to explain the action, including their gender (e.g., probably a man) and mental state (e.g., probably angry).
the
Affect Control Theory (ACT) is rigorously evaluated sociological model of impression formation that theorizes that impressions are largely determined by the words people use
* Equull contribution.

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"champion" High-Volence
"insults" Low-Vallnn Valenee Dominance High-Arousal
"servant" High Valence Deminancc
male"
female"
High Nan--Darinencce HighsArousal
femining
Lrousroussal
MMll"" "
Top-k likely
A <actor> <behavior> <recipient>,
Mann
tokens
The actorr>ss gender is more likely to be [BLANK]
"he
Figure 1: ACT-inspired prompt generation to assess gender impressions in LLMs
to label situations (Heise, 2007). ACT abstractly summarizes social situations terms of "sentences" made up of an Actor, Behavior, and Recipient (ABR)-e.g, "bully mocks scientist"-given a dictionary of labels that hold shared meaning, within a specific culture or group. Whether actor is labeled, for example, as "teacher" expectations about their character and the actions they are likely to perform. When actors deviate from these expectations, called deflection, actors may be relabeled (e-g., an "angry friend") to explain and mathematically account for the deviation. This model captures social dynamics by making mathematical predictions that connect social action identities and labels, offering a richer understanding of what is socially normative.
s "friend" determines
Recent advancements in language models (LLMs) have enabled their application in critical societal domains such as healthcare, hiring, and education sducation-settings where modeling human impression formation is essential. However, prior work on Al impression formation has focused on expectations about social entities in isolation- e.g., homemakers are expected to be women, programmers, men, and criminals Black (Bolukbasi et al., 2016). In contrast, ACT captures how these isolated impressions change when formed in the context of social interactions. Thus, a natural question whether LLMs are similarly sensitive to the social context. If LLMs "think" like humans, expects them to reflect the culturally shared defaults that ACT assigns as most normative.
By systematically comparing LLM output to ACT predictions in well-studied case of gender impressions, we gain insight into how well these models approximate human im- pression formation in social interactions. Specifically, we examine how human alignment post-training or preference funing methods, such as Supervised Fine-tuning (SFT) and Rein- forcement Learning from Human Feedback (RLHF), influence model impression formation. Previous research has extensively studied the potential negative impacts of preference tuning and RLHF (Lambert al., 2023). For instance, models are aligned primarily with English-speaking users (Ouyang et al., 2022). RLHF is shown to steer models toward more assertive outputs (Hosking et al., 2023), reduce novelty in responses (Kirk et al., 2023), make mistakes in outputs more subtle (Bai et al., 2022a), lead to models echoing user opinions or expressing stronger political views (Perez et al., 2022), and create disparities between English dialects and global perspectives (Ryan et al., 2024). Inspired by the ACT framework, we create a benchmark dataset for social interaction impression formation and evaluate Llama-3.1 along with its fine-tuned derivatives, using GPT-4 baseline. Our findings reveal that alignment processes can shift LLM gender impressions in inconsistent and di- vergent ways across models. We replicate the experiments using the larger 70B- (0B-parameter variants and find that the trends remain consistent across scales. Thus, we specifically address the following questions: RQ1 Do LLMs show patterns of impression formation in gendered social interactions that are consistent with ACT predictions? RQ2 How does alignment or post-training shape or distort these patterns?
By focusing on dynamic social interactions, our analysis provides a deeper understanding of how cultural impressions pppressions-spcfffilllll gender impressions are encoded in LLM out- puts. This approach offers a foundation for refining model tuning processes. Ultimately, these insights can help develop LLMs that more accurately reflect human-like impression formation while reducing unintended distortions and inconsistencies.
2 Background
2.1 Affect Control Theory
Many theories of impression formation use dimensional models to characterize impressions. For example, Oosterhof & Todorov (2008) characterize impressions formed from faces into
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the orthogonal dimensions of valence (good vs. bad) and dominance (powerful weak). While using slightly different names, ACT also uses valence, dominance, and additionally, arousal (active to passive), resulting in three-dimensional impression space (VAD: Valence, Arousal, Dominance). These ratings, collected across cultures, are compiled into dictionaries of average affective meanings (Heise, 2007). These dictionaries include mean VAD ratings for thousands of social interaction concepts, demonstrating strong intra-cultural consensus and stability over time (Rogers al., 2013; Heise, 2010).
ACT posits that individuals seek to confirm fundamental sentiments--culturallly shaped context-free expectations about themselves, others, and situations social interactions. Deflection, measured as the squared Euclidean distance between contextualized impressions and context-free fundamental sentiments in VAD space, quantifies event unexpectedness. More deflection means more surprise- e.g., mother yelling at her child mismatches expected nurturing norms (Rogers al., whe low deflection indicates the event is normative for the actor (Heise & MacKinnon, 1987; Heise & Lerner, 2006; Schröder et al., 2013b; Schröder & Scholl, 2009).
2.2 Social gender impressions
Whether examined through the lens of ACT (Langford & MacKinnon, 2000; Rogers al., 2013) or other related theories (Cikara & Fiske, 2009), research shows the perception of masculinityy is linked to power, competence, and dominance, while femininity is associated with goodness, caring, and warmth (Freeland & Harnois, 2020; Fraser et al., 2021; 2022; Nicolas & Caliskan, 2024a). Women are often seen as less agentic and more communal than men, with agency tied to leadership and competence, and communality linked to helpfulness and emotional expression (Heilman, 2012; Wynn & Correll, 2018).
These stereotypes shape both expected (prescriptive) and observed (descriptive) behaviors (Fields, 2023). Notably, masculine traits associated with dominance are rewarded with higher wages, highlighting its influence on occupational income, while valence shows no such effect (Freeland & Harnois, 2020). Dominance is frequently interpreted not only as a measure of power but also representation of an identity's ' competence within workplace contexts (Cuddy et al., 2008; Rogers et al., 2013). Fields (2023) used ACT to model the ways in which men and women lawyers are perceived when enacting behaviors in their daíly practice and showed cultural meanings maintain gender status hierarchies in the field. ACT simulations by Boyle (2024) show that criminal behaviors generate higher deflection for women than men, with larger gender gaps aligning with greater disparities in crime rates based on multiple crime data sources.
2.3 LLM Impressions and Stereotypes
Impression formation in language generation can manifest at both local and global levels (Gallegos et al., 2024). Local effects refer to word-context associations, such as differences in next-token likelihoods-edt e.g., "men/ women are known for [BLANK]", while global effects involve valence or other properties over a span of generated text (Sheng et al., 2019; Nangia et al., 2020; Liang et al., 2021; Yang et al., 2022). Word embeddings offer another way to examine these phenomena in LLMS by numerically encoding semantic relationships and capturing stereotypical associations (Caliskan et al., 2017). Primarily focused on valence- oriented analyses, prior research uses embeddings to identify keywords linked to social categories and analyze them with dictionaries (Nicolas & Caliskan, 2024a; Charlesworth et al., 2022). Loon & Freese (2023) show that despite distinct purposes of embeddings and VAD catinis-capturin the semantic relationships of words vs. measuring consensual affective connotations of concepts-, embeddings can serve as proxy for VAD ratings.
In examining gender impressions, Caliskan et al. (2022) analyzed English word embeddings and found a masculine default: male-associated terms were predominantly verbs (e.g,, fight, overpower) linked to tech, religion, sports, and violence, whereas female-associated terms were mainly adjectives/ adverbs (e.g., giving, emotionally) tied to slurs, appearance, and domesticity. Farlow et al. (2024) observed no explicit bias in ChatGPT's reference letters but noted a male-centric language, potentially stemming from historical associations between men and leadership. Busker et al. (2023) reported that ChatGPT perpetuates gender stereotypes (especially in question-based prompts), while Zhao et al. (2024) identified
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communal descriptors tied to female roles in GPT-4. Bai et al. (2024) similarly found GPT-4's implicit skew, associating STEM fields with boys and humanities with girls.
LLMs are shown to inherit often stereotypical social perceptions due to training on uncu- rated, Internet-based data (Benjamin, 2019; Bender et al., 2021; Dodge et al., 2021; Sheng et al., 2021; Barocas et al., 2023). However, efforts to align models with human values can produce unintended consequences as illustrated by the controversial Gemini case, where attempts to promote diversity paradoxically resulted in the generation of images depicting Black Nazis (Field, 2024). Such instances highlight the emergence of "surprising" or "unexpected" biases. Wilson & Caliskan (2024) simulated resumee screening using retrieval method and found that LLMs reinforced societal racial and gender "defaults" that do not necessarily reflect real-world occupational patterns. Takemoto (2024) found that GPT-3.5 and GPT-4 mirrored human tendencies in moral dilemmas but amplified biases, such as a stronger inclination to save females. Fulgu & Capraro (2024) suggest that efforts to address gender parity in recent GITT iterations may inadvertently create extreme disparities as models consistently reinforced feminine stereotypes and attributed masculine stereotypes to females. Spillner (2024) showed ChatGPT often assigns female characters to occupations traditionally associated with men. Additionally, GPT-4 portrays sixeddgender violence more acceptably for female actor and male victim (Fulgu & Capraro, 2024).
3 Method
3.1 Impression Formation Benchmark Dataset
Following an established practice in ACT research Shank & Burns (2022); Lulham & Shank (2023), construct diverse set of events spanning all possible combinations of sentiments for actors, behaviors, and recipients (ABR profiles). Specifically, we generate 2 2 2 sentiment configurations, constrained by high/low VAD values. We select four representa- generate synthetic events, systematically
tive items per configuration from the dictionary covering all possible interactions (see Table 3 in Appendix B). We utilize cultural meanings from the USA Combined Surveyor Dictionary (Smith-Lovvn cultural sentiments in each generated scenario, aligning with Western social impressions that LLMs are also largely trained on (Cao et al., 2023). This dictionary is created using ratings from participants on 2,402 social Modifiers-aaroos VAD dimensions. Due to the absence of certain ABR profiles in the behavior dictionary, we generate total of 32 26 31 25,792 unique events. For example, "scientist insults trainee" represents an A: HHL B Hg R. HoLaLD profile, where H/L High/Low VAD scores.
al., 2016) to evaluate the
Identities, Behaviors, and
3.2 Model Selection and Prompt Design
SFT
DeepSeck R1-8B
To examine the impact of alignment efforts on gender impression-ins i.e, how models en- code the gender-dependent normativeness of social interactions- it is imporlant to
SFT- RLHF
Llama-3.1 Instruct
Llama-3.1-8B
consider the role of alignment procedures, including SFT and RLHF (Ouyang et al., 2022). During the SFT stage, models are trained on human-written example comple- tions, fine-tuning them to generate similar responses. The main goal of SFT is make language models follow instructions, rather than simply continuing the input text (Ryan et al., 2024). Following SFT, models un- dergo preference tuning using two widely used algorithns-PPO (Schulman et al., 2017) and DPO (Rafailov et al., 2024)-whers preference-ranked completions are used to further align LLMs with user expectations. Lambert et al. (2024) introduced RLVR as an added post-training step, to further fine-tune models on verifiable rewards (e.g., Math).
SFT
RLHF
RLVR
Tulu-3-0B SFT
Tulu
T.lu-3.1
DPO
Figure 2: 8B model variants evaluated in this work, illustrating their fine-tuning hierarchy. Appendix D details 70B model variants.
Following Ryan et al. (2024), we experiment with six mid-sized language models from the LLaMA 3.1 8B (Dubey et al., 2024) family and its derivatives that balance efficiency
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Hale Farmate Liaentr
-194
Actor Dominance
2.1
Behawter Valence
-2
-1A
Actor Valence
Actor Valence
Within-actor sentiment interaction (Aą x Ag)
Actor-Behavine valence interaction (Ap x Bu)
Figure Illustrates how social roles and actions shape gender impressions. Left illustrates impressions shape by roles alone: Roles seen as positive but weak (a servant) are assumed to be fulfilled by female actor. Right shows that roles interact with actions: actors that
perform good deeds are seen as female, especially if they are positive roles.
and performance, including LLaMA Instruct (Dubey et al., 2024), Tulu-3-SFT /ORT/DDO/RLV (Lambert et al., 2024), and DeepSSee-RI--isttilleed (Guo et al, 2025). With the exception of DeepSeek-RI, all models underwent both SFT and preference tuning (see Figure 2). Additionally, examine the intermediate Tulu models, since the intermediate checkpoints of other models have not been publicly released. We also include GPT-4 (Achiam et al., 2023) baseline model, given its status one of the most powerful and widely used LLMs. These selected models represent a range of post training and preference tuning methods, feedback sources, and training datasets, with variations in whether they were directly optimized via human or AI feedback (e.g,, GPT-4). See Appendix A for details on the alignment practices employed by the models used in this study. We repeat the experiments with the larger and more powerful 70B-pan 70B-parameter model variants from the same family of models to assess the impact of scale on our findings.
We adopt a probability- phrases to initiate text generation in manner that mirrors natural language production (Dhamala et al., 2021; Tang et al., 2024). Given a seed prompt (as shown in Figure 1)² we extract the top 20 most probable next tokens along with their probabilities. From these, we identify and sum the probabilities of gender-associated tokens (e.g., "man," "woman," " "male," " "female") represented py and Pm-- Inspired by (Dhamala et al., 2021), the LLM P
approach to measure LLM gender impression (GP), using seed
Gender Impression polarity measure is then computed as: GILLM = Py+
positive score
indicates a leaning toward female-associated tokens, while a negative score indicates leaning toward male-associated tokens. To reduce prompt dependency, repeat this with different templates and use the average scores across prompt variations (see Appendix B).
3.3 ACT-driven gender impression
Given = (Ap, Aa, Ad, Bd, Ba, B&, R2, Ra, R the vector representation of the fundamental centiments-and vector t = (Áp, A,, Aa BN, B,, Ba, R2, R₂ Ra) that is the post-event contex- tualized sentiments, event likelihood can be derived as:
Rd
R
Event Likelihood &
Σ i=
w.D:
Σ
w 1;)
(1)
where w stands for summation weights, and D, are ABR deflections. The equation demon- strates an empirically validated proposition that an event seems more likely when it gen- erates smaller deflections (Heise, 2007). To ensure consistency with the approach used to assess the next-token probability assigned by language models, we define the ag- gregate concept vector representing female and male vector positions in the VAD space
1GPT-4 required structured JSON prompt, unlike the tested models, as the next-token setup could not bypass its guardrails and it largely avoided queries. See Appendix B for further details.

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Effect
ACT
GPT-4
Llama-
Llama- Instruct-SB
Tulu-3-
Tulu-3-
Tulu-
DeepSeek
3.1-8B
8B-SFT
SB-DPO
3.1-8B
-R1-8B
Actpr
.25
41
.13
.16
.11
.11
.11
.06
Beltpr
.0
.04
.00
.01
.00
.00
.00
.00
Recpr
.05
.08
.01
.02
.02
.01
.01
.00
Actpr
.22
.00
.00
.00
ns
.00
.00
x Recppr
.08
.00
ns
.00
.00
.00
.00
.00
xeehrr
.21
.02
ns
ns
ns
3-way int.
.13
ns ns
ns
Effect sizes color-coded as: insignificant, small, medium, large, and very large. All results are significant (p value < 0.05) unless marked ns. Values below r/2 < .01 are shown as .00.
Table 1: Effect sizes (partial r7²) for gender impression variations across ABR profiles (e.g., Act = Actor profile). Actor profiling is the primary driver of gender impression across LLMs, with social context sensitivity, predicted by ACT.
(see Appendix B for further details). To quantify gender impression, we set the weighting parameters w WA W, to 1, while assigning all other weights to O. This configuration allows us to focus solely on the actor's deflection, examining whether gendered actors are perceived differently within the ACT framework. We define the ACT Gender Impression polarity measure GLACT as:
AA,
Ad GLACT Σ i
A4 Σ A
(falle - -2,)2- (female. t,)2
(2)
This formulation captures the propensity of an event's actor to be associated with female identity rather than a male identity. Specifically, it measures the sentiment distance of the actor after the event occurs, relative to male and female positions in the VAD space. A higher GLACT indicates that more normative or plausible for female be the actor in specific interaction compared to a male. Earlier ACT research sets thresholds for significant gender impression (Heise, 2007; Fields, 2023; Boyle, 2024; Kroska Cason, 2019).
Leveraging empirically derived impression formation equations (Heise, 2007), we identify conditions where gender impression is neutral, i.., GLACT = O. We illustrate this Figure 3, where we analyze two key interactions: A. x A and Ap x B, (with all other variables set to zero). Figure 3 shows that as Ag increases and A, decreases, the actor's contextualized impression shifts closer to the female aggregate position in VAD space. This finding aligns with previous studies associating women with warmth but lower power. Examining the interaction of an actor performing a behavior, we see higher B, is more closely associated with female identities. This suggests that the more positive the actor and behavior, the closer their contextualized impression aligns with the aggregate female vector. This experiment establishes a theoretically grounded continuous measure of gender impression in social interactions while controlling for other components (e.g., recipient and setting).
Results
4.1 LLMs Ignore the Actor's Behavior When Forming Impressions
We first investigate how ACT and LLM-derived gender im impressions (GlACT and GILLM) vary in generated scenarios. We categorize scenarios based on ABR profiles for each event (high or low for V, A, and D), yielding factors Actpr, Behpr, and Recpr. We use a three-way ANOVA to test whether gender impression varies across ABR profiles (Actpr, Beh Rec,r). Table shows all main effects, two-way and three-way interactions are significant (p .05) for ACT, while Recipient-Behavin
and three-way interactions are not significant across tested models. Given the large number of events, statistical significance less informative, so we emphasize practical significance through effect sizes. Notably, in ACT, the Behtp
shows the largest effect size =57), followed by Actpr 225). The three-way interaction remains significant (F(294) = 13.15, < .001, 1/2 .13), indicating that combinations of ABR profiles jointly influence whether ACT shows female male tendency.

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GPT-4 displays notably large effect for actor profiling (17² 0.41), indicating that impres- sion is strongly driven by which type of actor (Acfpr) is involved. By contrast, LLLMA-3.1--0 LLaMA-Instruct, and Tulu variants show substantially smaller effects for Actpr (0.11-0.16). DeepSeek-
exhibits very low effect sizes for any ABR configuration, suggesting that it remains largely insensitive to the scenario context. Table also shows that behavior (Behpr) and recipient (Recpr) contribute little to GlLLM across models: effect sizes hover near or below 0.01, and most interactions are non-significant. This pattern indicates minimal sensitivity to contextual elements beyond the actor's 's identity.
Overall, ABR profiles partils--prticularlyy Actpr and Behpr a substantial role in modulating ACT's gendered tendencies. The largest effect size for B, is noteworthy, suggesting that behaviors, not only actor identity, drive female vs. male impressions when following ACT's empirically validated predictions. Moreover, the interaction effects indicate that ACT's predicted gender impressions are context-dependent and cannot be fully explained by static identity-basee -baeed metrics (e.g., "doctor" vs. "nurse"). Although LLMs largely failed to account for interaction effects and contextual elements in impression formation, they align with ACT in assigning lower importance to the main effect of the recipient. A strong main effect of the recipient would imply that impressions of the actor are shaped primarily by the recipient, rather than by the broader context of the interaction. When repeating the experiment with the 70B-variants we observe very similar overall results. However, one notable difference is that DeepSeek-R1-70B shows sensitivity to actor profiles comparable to the other LLaMA-3 derivatives (see 70B-variant results in Appendix D).
4.2 Valence and Dominance Drive Impression Formation; Arousal Plays No Role
To dissect why actor profiling exerts such a strong effect on GlLLM, we fit linear mod- els to study how High (H) and Low (I.) levels of actor's VAD predict GIACT and GILLM- Table 2 presents the effect sizes for the actor's 'ss VAD components across mod-
Model
Ay
Aa
Aa
ACT
.05
.00
.02
GPT-4
.21
.00
.22
Llama-3.1-8B
.08
.00
.01
Llama-Instruct-8B
.10
.00
.01
els. In ACT, Ag (p < .001, ,/2 .05) and Aa (p < .001, 2 .02) exhibit significant effects, while arousal and higher-order inter- actions are not significant or exhibit small effect sizes (See Appendix C for complete results). Notably, GPTT4'ss s's exhibits large ef- fects for A, and A, (0.21 and 0.22, respec- tively), whereas LLaMA-3.1 1-B---B--BB--- effect sizes for these dimensions are more moderate (0.08 and 0.01) and comparable to ACT. The fine-tuned variants display comparable but varied sensitivities.
Tulu-3-8B-SFF
.06
.00
.02
Tulu-3-8B-DPO
.04
.00
.02
Tulu-3.1-8B
.05
.00
.02
DeepSeek-R1-99
.01
.00
.00
All results are statistically significant (p .05).
Values < .01 are shown .00.
Table 2: Effect sizes (partial 17²) for actor's s sentiment dimensions in predicting gender impression. Arousal has no effect across all models, with GPT-4 being the only model de- viating from ACT predictions.
These findings suggest that variations in valence and dominance alone shift gender impres- sion, while arousal plays minimal role. LLMs appear align with ACT in assigning lower importance to actor arousal levels and actor sentiment interactions when forming gender impressions, a finding consistent across model scales (see Appendix D).
4.3 Alignment Has Unintended Consequences
We next compared the overall GIJJ.M across LLMs. A one-way ANOVA indicated that GI differs significantly by model, F(6, 180537) = 11561, p < .001, with large effect size of 2 = .28. Post-hoc comparisons (Tukey's s HSD) show that GPT-4 produces strongly positive mean GlLLM of + +.19,, significantly exceeding LLaMA-3.1-81 by +.50 points (p < .001). In contrast, most other models yield negative means: LLaMA-Instruct (-.4), Tulu-3.80 3.BBSSF
(2),), Tulu-3.8B 1iai-3..BB--OO--- (- -56), Tulu-3.1-8B (-.49), and DeepSeek-R1 R1-BB (-.66) - all more male-leaning than LLaMA-3.1-8B (p < .001).
Due to the stronger effects observed for actor and behavior profiles, we omit the recipient from this analysis. We further examined whether LLMSs exhibit greater shifts for "good" vs.
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ACT
OPT4
Liama 3.1-88
Lime-Instnuc-e0
Tule-s-98 1------8---0 SP-SPT
Tulu T-M---B8.DPP
Tulle 5.1-88
DuopSeek.Ak
Figure Effect of actor valence (Ap) on gender. Positive actors are more likely to be seen as female, but LLaMA-tuned variants are far more likely to assume all actors are male.
ACT GPT4
Liama-11-08
Lama-inttruct-00
Tule-308--FF
Tul-3-BB-OPO
Tulo-b33-00
DeepSeel-21--0
105
Figure 5: Effect of actor dominance (A₂) gender impressions. Dominant actors are
assumed to be male though GPT-4 deviates from ACT, assuming women are more dominant.
"bad" (H/L. valence) and for "powerful" vs. "weak" (H/L. dominance) actors and behaviors. As summarized in Figure 4, GPT-4 shifts from slightly negative when the actor is "bad" (03) to strongly positive for "good" (H, +.3),, while Tulu variants and DeepSeek-RI-8B remain male-leaning across both actor types. Although all models show a higher female tendency for H vs. L, the magnitude of this shift varies significantly, with GPT-4 experiencing the largest swing. Figure shows that consistent with ACT impressions, all LLaMA derivatives assign higher dominance to male actors; however, GPT-4 notably diverges from this pattern, showing a reversed impression with females perceived as more dominant.
Overall, these findings reinforce that post training and alignment consistently accentuates male-leaning impression: LLMMA-Instruct, Tulu variants, and especially DeepSeek-RI skew strongly male. Yet, GITT-4, which has been used as a reward model for some fine-tuned variants above, is strongly female-biased. These results indicate that different models not only differ in the direction of their impression (with GPT4 being female-leaning and the others male-leaning) but also differ in the extent to which specific stimulus dimensions contribute to their impression formation. All observed trends remain consistent in the larger 70B variants (see Appendix D for details).
4.4 LLM impressions differ substantially from ACT
Although we observe positive correlations between ACT's predicted gender impressions (GIACT) and each LLM's GILLM, these correlations remain modest. Specifically, linear regressions of the form GlLLM ~ GLACT yield the following R² values: about 1% of variance (R² = .01) in GPT-4 is explained by GIACT, roughly 1.5% in LLaMA-3.1-8B, 4.3% in LLaMA- Instruct, 2.8% in TulF-,.8 Tulu-3.8B-DPO 2.4%, Tulu-3.1-8B 2.9%, and DeepSeek-R1-8B 1%. Although ACT's empirically validated scores align directionally with each model's gender impression, they account for only small fraction of the variability. These findings underscore that ACT-based impressions alone cannot foretell exactly how a given LLM will behave; factors such as instruction-tuning data, RLHF procedures, or domain-specific corpora exert a substantial influence on the final gender impression.
5 Discussion
By modeling how people form judgments about others in social interactions, ACT offers theoretical Paseline-roote rooted in empirical cultural sentiment norms for assessing where LLMs' predictions might diverge. Using designed impression formation benchmark dataset, we reveal that actor is the primary driver of gender impression in all tested LLMs; most models largely ignore the belavvir performed and the recipient involved. As result,
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these models exhibit rigid, actor-centric stereotypes pattern reminiscent of the funda- mental attribution error in humans, where individual dispositions overshadow contextual factors (Smith, 2002; Rogers, 2018; Malberg et al., 2024; Rzadeczka et al., 2025). While ACT posits that behaviors and contexts significantly shift an event's cultural plausibility, the LLMs we studied fail to incorporate such nuances, diverging from the interaction- reasoning ACT prescribes.
This is significant since LLMs play role in shaping social perceptions and influencing how identity meanings evolve. When identity meanings are fluid and responsive to context, low-status individuals can challenge stereotypes through social actions. However, when identity and behavior meanings are rigid and cultural narratives reinforce consistency, stigmatized individuals face persistent negative perceptions, while high-status individuals remain shielded from the consequences of their actions (Link & Phelan, 2001; Hunzaker, 2016; Robinson et al., 2020). This behavior may stem from LLMs being trained primarily on English-language data and Western cultural norms (Cao et al., 2023; Tao et al., 2024). Cross- cultural research highlights key differences: in high-context cultures (e.g,, China), identity impressions are heavily shaped by behaviors and interactions, leading to greater flexibility in impression formation, whereas in low-context cultures like the U.S., identity meanings remain stable regardless of situational cues, showing stronger adherence to cultural norms (Rogers, 2018; Kriegel et al., 2017; Zhao, 2023).
cultural
Our study also shows that post-training inteeneed to make models safer and more aligned-introdiee
or amplifies gender impression shifts in unpredictable ways. Past research has similarly noted that RLHF does not fully account for subtle or context-sensitive stereotypes (Achiam et al., 2023; Nicolas & Caliskan, 2024b). While GPT-4 generally displays a female-leaning impression (though reversing the common association of dominance with males), fine-tuned LLaMA derivatives consistently become more male-leaning across both the 8B and 70B model scales. Such representations can disproportionately affect different demographic groups, highlighting the need for transparent reporting on how and why certain alignment procedures are adopted (Mitchell et al., 2019; Ouyang et al., 2022; Longpre et al., 2023; Ryan et al., 2024). Without clarity on annotator demographics, prompts, and do- main coverage, alignment pipelines risk reproducing unfounded associations. The Llama technical report Touvron et al. (2023) acknowledges the limitations of automatic evaluations, emphasizing the subjectivity of human raters and benchmark constraints in fully capturing the complexities of stereotypical representations. Similarly, Ouyang et al. (2022) note that addressing misrepresentations is inherently challenging, as well-intentioned interventions in LM behavior can lead to unintended side effects. As seen with InstructGPTT, strong alignment with human preferences does not necessarily equate to reduced stereotypical social impressions; in some cases, it amplified stereotypes with even greater confidence.
Unlike many studies focusing on valence and dominance in forming gender impressions, earlier work has underscored the importance of arousal (e.g., activity or expressiveness) for distinguishing stereotyped groups (Schröder et al., 2013a), yet neither LLMs nor our ACT-driven analyses revealed significant attention to the arousal dimension. While ACT ac- knowledges that certain groups or roles vary substantially in their arousal (e.g., entrepreneurs are "active," intellectuals "passive" (Freeland & Hoey, 2018)), the tested LLMs gave only marginal weight to such distinctions. This suggests fundamental gap between LLMs' internal representations and real-world cultural schemas involving arousal or dynamism.
While our findings highlight systematic, actor-centric patterns in LLM impression formation, several boundaries of the present work should be acknowledged. First, our evaluation relies on the U.S. ACT dictionary and English prompts, constraining conclusions to Western cultural norms and overlooking cultures where behavior- and context-sensstive impres- sion formation may differ. Second, we studied only gendered impressions within simple Actor-Behavior-Reiiiiee events, leaving other social dimensions and more complex multi- party or self-directed interactions for future research. Third, ACT post-event estimates provide theory-driven approximations of human judgments, but we did not collect new human ratings to revalidate ACT predictions or the LLM outputs. Lastly, although all evalu- ated models underwent general safety or preference tuning, we did not isolate alignment protocols purpose-built for debiasing, so our results cannot pinpoint which post-training strategies mitigate (or amplify) actor-centric biases.
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Future work can broaden these analyses to other forms of impression formation (e-g., other demographic groups, social status, political orientations, trait attributes, and emotional states) and leverage ACT's potentill in modeling modifiers or more complex social situations. High-deflection scenarios-thorr those that clash with cultural expectations -oten prompt the introduction of modifiers (e.g., "angry teacher beats studenf") to reduce deflection, thus realigning an event with cultural sentiments. LLMs' strategies for deflection reduction remain unexplored; investigating whether their adjustments mirror cultural patterns in ACT could reveal new insights into emergent stereotypes. To our knowledge, this study is the first to apply ACT deflections as a measure of social interaction plausibility in LLMs, highlighting how deeper, interactional constructs of impression can be evaluated and compared against empirically grounded cultural norms.
Ethics Statement
This study examines how preference tuning in LLMs impacts gender biases in social interac- tions. Our goal is not to make value judgments about biases or their societal consequences but rather to highlight how alignment helpful-can unintentionally accentuate or shift biases in unpredictable ways. While previ- ous work has demonstrated the real-world implications of biased AI outputs, we show that alignment to human values does not necessarily mitigate biases and may, in some cases, reinforce or reorient them. Importantly, many alignment procedures prioritize benchmarks related to safety, helpfulness, and instruction following, often with limited attention to bias impacts. This underscores the need for transparency in reporting alignment decisions, as these processes introduce significant normative choices that shape model behavior.
-ineessee--tteeedd to make models safer and more
By modeling social perception through Affect Control Theory (ACT), we provide theoreti- cally grounded framework for assessing social interaction bias in LLMs. Understanding how LLMs encode these biases critical, as Al systems increasingly mediate real-world decision-making processes in hiring, healthcare, and other domains. We emphasize the importance of continued scrutiny of alignment methods and encourage further research into debiasing strategies that account for context-sensitive, socially informed reasoning.
Acknowledgments
This work is supported by the Army Research Office under Cooperative Agreement Number W911NNF-2522-004 Only staff at ICT were sponsored directly by the Army Research Office. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes, notwithstanding any copyright notation herein.
References
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint ar arXiv:2303.08774 2023.
Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al. Palm technical report. ar Xiv preprint ar arXi::2305.1044 2023.
Xuechunzi Bai, Angelina Wang, Ilia Sucholutsky, and Thomas L Griffiths. Measuring implicit bias in explicitly unbiased large language models. arXiv preprint XXix:2402.04100 2024.
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. Training helpful and
10

---

Published as a conference paper at COLM 2025
harmless assistant with reinforcement learning from human feedback. arv preprint urXio:220400886,, 2022a.
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. Constitu- tional ai: Harmlessness from ai feedback. arXiv preprint ar arXiv:2212.08073, 2022b.
Solon Barocas, Moritz Hardt, and Arvind Narayanan. Fairness and machine learning: Limita- tions and opportunities. MIT press, 2023.
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pp. 510-623, 2021.
Benjamin. Race after Technology: Abolitionist Tools for the New Jim Code. Cambridge and Medford: Polity Press, 2019.
Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. Man is to computer programmer as woman is to homemaker? debiasing word embed- dings. Advances in neural information processing systems, 29, 2016.
Kaitlin M Boyle. Doing gender, avoiding crime: the gendered meaning of criminal behavior and the gender gap in offending in the united states. Social Psychology Quarterly, 87(2):
131-151, 2024.
Tony Busker, Sunil Choenni, and Mortaza Shoae Bargh. Stereotypes in chatgpt: empirical study. In Proceedings of the 16th International Conference on Theory and Practice of Electronic Governance, pp. 24-32, 2023.
Aylin Caliskan, Joanna| J Bryson, and Arvind Narayanan. Semantics derived automatically from language corpora contain human-like biases. Science, 356(6334):183-186, 2017.
Aylin Caliskan, Pimparkar Parth Ajay, Tessa Charlesworth, Robert Wolfe, and Mahzarin R. Banaji. Gender bias in word embeddings: A comprehensive analysis of frequency, syntax, and semantics. In Proceedings of the 2022 AAAT/ACM Conference on AI, Ethics, and Society, ATES 22, pp. 156-170, New York, NY, USA, 2022. Association for Computing Machinery. ISBN 9781450392471. doi:
Yong Cao, Li Zhou, Seolhwa Lee, Laura Cabello, Min Chen, and Daniel Hershcovich. Assessing cross-cultural alignment between chatgpt and human societies: An empirical study. Xiv preprint ar Xiv:2303.17466 2023.
Tessa ES Charlesworth, Aylin Caliskan, and Mahzarin R Banaji. Historical representations of social groups across 200 years of word embeddings from google books. Proczedings of the National Academy of Sciences, 119(29))e112199811, 2022.
Gary Charness and Uri Gneezy. Strong evidence for gender differences in risk taking, Journal of economic behavior & organization, 83115550-58, 2012.
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep reinforcement learning from human preferences. Advances in neural information processing systems, 30, 2017.
Mina Cikara and Susan T Fiske. Warmth, competence, and ambivalent sexism: Vertical assault and collateral damage. American Psychological Association, 2009.
Amy JC Cuddy, Susan T Fiske, and Peter Glick. Warmth and competence as universal dimensions of social perception: The stereotype content model and the bias map. Advances in experimental social psychology, 40:61-149, 2008
Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, and Rahul Gupta. Bold: Dataset and metrics for measuring biases in open-ended language generation. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pp. 2021.
11

---

Published as a conference paper at COLM 2025
Jesse Dodge, Maarten Sap, Ana Marasovié, William Agnew, Gabriel Ilharco, Dirk Groen- eveld, Margaret Mitchell, and Matt Gardner. Documenting large webtext corpora: case study the colossal clean crawled corpus. arXiv preprint ar arXio:2104.08758, 2021.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The llama herd of models. Xiv preprint ar Xiv:2404 217783 2024.
Janice L Farlow, Marianne Abouyared, Eleni M Rettig, Alexandra Kejner, Rusha Patel, and Heather A Edwards. Gender bias in artificial intelligence-written letters of reference. Ololaryngology-Hern and Neck Surgery, 2024.
Matthew Field. From black nazis to female popes and american indian vikings: How ai went 'woke'. The Telegraph, 2024.
Benjamin Fields. Acting like lawyer: An affect control theory explanation of gender gaps in law. Socius, 922370023133126629,, 2023. doi: 10.1177 10.1177//378003112311166
Kathleen C Fraser, Isar Nejadgholi, and Svetlana Kiritchenko. Understanding and coun- tering stereotypes: A computational approach the stereotype content model. arXiv preprint ar xxxi:21066002596 2021.
Kathleen C Fraser, Svetlana Kiritchenko, and Isar Nejadgholi. Computational modeling of stereotype content in text. Frontiers artificial intelligence, 5:826207, 2022.
Robert E Freeland and Catherine E Harnois. Bridging the gender wage gap: Gendered cul- tural sentiments, sex segregation, and occupation-level wages. Social Psychology Quarterly, 83(2):129-151, 2020.
Robert E Freeland and Jesse Hoey. The structure of deference: Modeling occupational status using affect control theory. American Sociological Review, 83(2):243-277, 2018.
Raluca Alexandra Fulgu and Valerio Capraro. Surprising gender biases in gpt. Computers in Human Behavior Reports, pp. 100533, 2024.
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, and Nesreen K. Ahmed. Bias and fairness in large language models: A survey. Computational Linguistics, 5099)190771, 09 2024. ISSN 0891-2017. doi:
10.1162/col coli 00524.
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-rl: Incentivizing reasoning capability in lims via reinforcement learning. arviv preprint XrX1::2001.12945 2025.
Svetlana D Gurieva, Tatiana V Kazantseva, Larisa V Mararitsa, and Olga E Gundelakh. Social perceptions of gender differences and the subjective significance of the gender inequality issue. Psychology in Russia, 15(2):65, 2022.
Seungju Han, Kavel Rao, Allyson Ettinger, Liwei Jiang, Bill Yuchen Lin, Nathan Lambert, Yejin Choi, and Nouha Dziri. Wildguard: Open one-stop moderation tools for safety risks, jailbreaks, and refusals of lims. Advances in Neural Information Processing Systems, 37: 8093-8131, 2025.
Madeline E Heilman. Gender stereotypes and workplace bias. Research in organizational Behavior, 32:113-135, 2012.
David R Heise. Expressive order: Confirming sentiments in social actions. Springer Science & Business Media, 2007.
David R Heise. Surveying cultures: Discovering shared conceptions and sentiments. John Wiley & Sons, 2010.
David R Heise and Steven J Lerner. Affect control in international interactions. Social Forces, 8592),993-1010, 2006.
12

---

Published as a conference paper at COLM 2025
David R Heise and Neil J MacKinnon. Affective bases of Mathematicaa Sociology, 13(1-2):133-51, 1987.
likelihood perception. Journal of
Lori Heise, Margaret E Greene, Neisha Opper, Maria Stavropoulou, Caroline Harper, Marcos Nascimento, Debrework Zewdie, Gary L Darmstadt, Margaret Eleanor Greene, Sarah Hawkes, Lori Heise, Sarah Henry, Jody Heymann, Jeni Klugman, Ruth Levine, Anita Raj, and Geeta Rao Gupta. Gender inequality and restrictive gender norms: framing the challenges to health. The Lancet, (5(10018):2440-222 2454, 2019. ISSN 0140-6736. doi: https://doi.org /tt../gg////////////// (0..1116/S0140-6766(9300000
Tom Hosking, Phil Blunsom, and Max Bartolo. Human feedback is not gold standard. arXiv preprint (rXxi:2309.163444 2023.
MB Fallin Hunzaker. Cultural sentiments and schema-consistency bias in information transmission. American Sociological Review, 81(6):123--1250, 2016.
Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette, and Roberta Raileanu. Understanding the effects of rlhf on Ilm generalisation and diversity. ar preprint ar Xie:2310.06452. 2023.
Darys Kriegel, Muhammad Abdul-Mageed, Jesse Clark, Robert E Freeland, David R Heise, Dawn T Robinson, Kimberly B Rogers, and Lynn Smith-Lovin. A multilevel investigation of arabic-language impression change. International Journal of Sociology, 47 (4):278-295, 2017.
Amy Kroska and Trent C. Cason. The gender gap in business leadership: Exploring an affect control theory explanation. Social Psychology Quarterly, 82(1):75-97, 2019
Nathan Lambert, Thomas Krendl Gilbert, and Tom Zick. The history and risks of reinforce- ment learning and human feedback. arXiv e-prints, pp. arXiv-2310, 2023.
Nathan Lambert, Jacob Morrison, Valentina Pyatkin, Shengyi Huang, Hamish Ivison, Faeze Brahman, Lester James V Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, et al. T ulu 3: Pushing frontiers in open language model post-training ar preprint 2r11:2441..15224 2024.
Tom Langford and Neil MacKinnon. The affective bases for the gendering of traits: Comparing the united states and canada. Social Psychology Quarterly, pp. 34-48, 2000.
Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, and Ruslan Salakhutdinov. Towards understanding and mitigating social biases in language models. In International Conference on Machine Learning, pp. 6565-6576. PMLR, 2021.
Bruce G Link and Jo C Phelan. Conceptualizing stigma. Annual review of Sociology, 27(1): 363-385, 2001.
Shayne Longpre, Robert Mahari, Anthony Chen, Naana Obeng-Marnu, Damien Sileo, William Brannon, Niklas Muennighoff, Nathan Khazam, Jad Kabbara, Kartik Perisetla, et al. The data provenance initiative: A large scale audit of dataset licensing & attribution in ai. arXiv preprint arXiv:2310.16787, 2023.
Austin van Loon and Jeremy Freese. Word embeddings reveal how fundamental sentiments structure natural language. American Behavioral Scientist, 67(2):175-200, 2023, doi: 10. 1177/0002764221 1066046..
Rohan Lulham and Daniel B. Shank. How "stuff" matters in affect control theory. American Behavioral Scientist, 67(2):270-289, 2023. doi:
Simon Malberg, Roman Poletukhin, Carolin M Schuster, and Georg Groh. A comprehensive evaluation of cognitive biases in llms. ar Xiv preprint ar XrX:::2410.15411, 2024.
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. Model cards for model reporting. In Proceedings of the conference on fairness, accountability, and transparency, pp. 220 220-229, 2019.
13

---

Published as a conference paper at COLM 2025
Moin Nadeem, Anna Bethke, and Siva Reddy. Stereoset: Measuring stereotypical bias in pretrained language models. arXio preprint arXio:2004.094550 2020.
Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R Bowman. Crows-pairs: A challenge dataset for measuring social biases in masked language models. arXiv preprint (IXXiv2010000133,, 2020.
Gandalf Nicolas and Aylin Caliskan. Directionality and representatiivenss are differentiable components of stereotypes in large language models. PNAS nexus, 3(11) 2024a.
Gandalf Nicolas and Aylin Caliskan. A taxonomy of stereotype content in large language models, 2024b.
Nikolaas N Oosterhof and Alexander Todorov. The functional basis face evaluation. Proceedings of the National Academy of Sciences, 105(32):1008--1102 2008.
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, al. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35:27730-27744, 2022.
Ethan Perez, Sam Ringer, Kamilè Lukošiüté, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Catherine Olsson, Sandipan Kundu, Saurav Kadavath, al. Discovering lan- guage model behaviors with moite-written evaluations. arviv preprint ar 2rXi0:2212.09251, 2022.
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn. Direct preference optimization: Your language model is secretly reward model, 2024. URL
1820.
Glenn D Reeder, Roos Vonk, Marla Ronk, Jaap Ham, and Melissa Lawrence. Dispositional attribution: Multiple inferences about motive-related traits. Journal of personality and social psychology, 86(4):530, 2004.
Dawn T Robinson, Lynn Smith-Lovin, and Jun Zhao. The role of the other: how interaction partners influence identity maintenance in four cultures. In ldentity and symbolic interaction: Deepening foundations, building bridges, pp. 213-237. Springer, 2020.
Kimberly B. Rogers. Do you see what see? testing for individual differences in impressions of events. Social Psychology Quarterly, 81(2):149-172, 2018. doi: 10.1177/01902251177666
Kimberly B. Rogers, Tobias Schröder, and Wolfgang Scholl. The affective structure stereo- type content: Behavior and emotion in intergroup context. Social Psychology Quarterly, 76 (2):125-150, 2013.
doi: 10.1177/019022251.33
Michael J. Ryan, William Held, and Diyi Yang. Unintended impacts of Ilm alignment on www.or///abs/222...
global representation, 2024. URL http::///arxw.orr//bb///0.. 15018.
Marcin Rzadeczka, Anna Sterna, Julia Stolinska, Paulina Kaczynska, and Marcin Moskalewicz. The efficacy of conversational ai in rectifying the theory-of-ming and autonomy biases: Comparative analysis. JMIR Ment Health, 12:e64396, Feb 2025. ISSN 2368-7959. doi: 10.2196/64339 64396.
Tobias Schröder and Wolfgang Scholl. Affective dynamics of leadership: An experimental test of affect control theory. Social Psychology Quarterly, 72(2):80-1977 2009.
Tobias Schröder, Janine Netzel, Carsten C Schermuly, and Wolfgang Scholl. Culture- constrained affective consistency of interpersonal behavior. Social Psychology, 2013a.
Tobias Schröder, Kimberly В Rogers, Shuichirou Ike, Julija N Mell, and Wolfgang Scholl. Affective meanings of stereotyped social groups in cross-cultural comparison. Group processes & intergroup relations, 16(6):717-73,, 2013b. 2013b.
14

---

Published as a conference paper at COLM 2025
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal .66477.
policy optimization algorithmss, 2017. URL https::/arx....rrg//aaaaa
Daniel B. Shank and Alexander Burns. How does employees' behavior change how we feel about their organization? transfer of affective impressions between employees and organizations. Social Science Research, 105:102723, 2022. ISSN 0049-089X. doi: 10.1016/j.
ssresearch.2022.10777
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. The woman worked babysitter: On biases in language generation. arXiv preprint 1Xx::1909.013266, 2019.
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng. Societal biases in language generation: Progress and challenges. arXiv preprint ar Xit:2105.004054 2021.
Herman W. Smith. The dynamics of japanese and american interpersonal events: Behavioral settings versus personality traits. The Journal of Mathematical Sociology, 26(1-2):71-92, 2002. doi: 10.1080/002222000111
Lynn Smith-Lovin and David R Heise. Analyzing social interaction. Advances in affect control theory. Gordon and Breach, 1988.
Lynn Smith-Lovin, Dawn T. Robinson, Bryan C. Cannon, Jesse K. Clark, Robert Freeland, Jonathan H. Morgan, and Kimberly B. Rogers. Mean affective ratings of 929 identities, 814 behaviors, and 660 modifiers by university of georgia and duke university undergraduates and by community members in durham, ne, in 2012-2014, 2016. Distributed UGA Affect Control Theory Website: http: htlp:///esearche franklin. uga. edu/act.
Laura Spillner. Unexpected gender stereotypes in ai-generated stories: Hairdressers are female, but so are doctors. Text2Story@ ECIR, pp. 115-128, 2024.
Rebecca Stewart, Breanna Wright, Liam Smith, Steven Roberts, and Natalie Russell. Gen- dered stereotypes and norms: A systematic review of interventions designed to shift attitudes and behaviour. Heliyon, 7(4), 2021.
Kazuhiro Takemoto. The moral machine experiment on large language models. Royal Society open science, 11 (2):2331393, 2024.
Kunsheng Tang, Wenbo Zhou, Jie Zhang, Aishan Liu, Gelei Deng, Shuai Li, Peigui Qi, Weiming Zhang, Tianwei Zhang, and NengHai Yu. Gendercare: A comprehensive frame- work for assessing and reducing gender bias in large language models. In Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Commutications Security, CCS '24 pp. 1196-1210, New York, NY, USA, 2024. Association for Computing Machinery. ISBN 9798400706363.. doi: 2..11553358644.333
Yan Tao, Olga Viberg, Ryan S Baker, and René F Kizilcec. Cultural bias and cultural alignment of large language models. PNAS nexus, 3(9):pgae346, 2024.
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Sounya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307 0.98889,0888 2023.
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. Finetuned language models zero-shot learners. ar Xiv preprint ar Xiv::20000001 652, 2021.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824-22 24837, 2022.
Kyra Wilson and Aylin Caliskan. Gender, race, and intersectional bias in resume screening via language model retrieval. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, volume 7, pp. 1578-1599 2024.
15

---

Published as a conference paper at COLM 2025
Alison T Wynn and Shelley J Correll. Puncturing the pipeline: Do technology companies alienate women in recruiting sessions? Social studies of science, 48(1):149-164, 2018.
Zonghan Yang, Xinoyuan Yi, Peng Li, Yang Liu, and Xing Xie. Unified detoxifying and debiasing in language generation via inference-time adaptive optimization. ar preprint ar arXiv:2210.0449) 2022.
Jinman Zhao, Yitian Ding, Chen Jia, Yining Wang, and Zifan Qian. Gender bias in large language models across multiple languages. arxiv preprint arXit:2433.00277 2024.
Jun Zhao. Modeling impression formation processes among chinese and americans. Ameri- can Behavioral Scientist, 67(2):240 266,, 2023. doi: 101117/002764221000033
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. Large language models are human-level prompt engineers. In The Eleventh International Conference on Learning Representations, 2022.
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A LLM alignment and social impression formation
To examine the impact of alignment on social impression formation by LLMs, it is crucial to consider the role of alignment and post-training procedures (such as SFT and preference- tuning) that are widely employed to steer model behavior toward human-preferred outputs.
Supervised fine-tuning enhances LLMs by fine-tuning them on diverse tasks, enabling zero-shot generalization (Wei et al., 2021). Prompts can be human- or model-generated (Zhou et al., 2022). A related approach, chain-of-thought prompting, improves reasoning by encouraging step-by-step explanations (Wei et al., 2022).
Preference tuning fine-tunes LLMs using reinforcement learning to align with human preferences (Christiano et al., 2017). Ouyang et al. (2022) show that combining instruction tuning with RLHF helps address factuality, toxicity, and helpfulness issues that scaling alone cannot fix. Recent models often automate aspects of RLHF using Al-generated self-critiques and ranking systems (RLAIF) to reduce human dependence (Bai et al., 2022b).
In this section, provide an overview of the alignment practices used in the models evaluated in this study.
A.1 Llama 3
Following Llama recipe (Touvron et al., 2023), Llama 3 (Dubey et al., 2024) incorporates safety-focused post-training to enhance model alignment and minimize harmful outputs. employs Safety SFT and Safety DPO to refine responses, particularly handling ad- versarial and borderline content. To improve content moderation and fairness, Llama 3 strategically balances safety and helpfulness data during fine-tuning, ensuring the model can differentiate between safe and unsafe requests while reducing false refusals. Larger models exhibit stronger safety learning capabilities, requiring less explicit safety data than smaller ones (Dubey et al., 2024). Identifying the exact ratio of safety and helpfulness data for post-training is reported a major challenge to maximize both objectives. However, Llama 3's technical report lacks detailed insights mitigating social biases, stereotypes, and misrepresentations. Dubey et al. (2024) has not disclosed their internal safety bench- marks, annotator task eligibility, annotator agreement and quality, acknowledging that their evaluations are not externally reproducible.
To gain more insights, we reference Llama 2's safety evaluation (Touvron et al., 2023). Llama 2 evaluated the safety of model generations from the perspectives of toxicity, truthfulness, and bias. To study bias, they are limited to study the sentiment variation in model gen- erations across demographic attributes using BOLD (Dhamala et al., 2021) dataset, a bias benchmark that comprises 23,679 English Wikipedia prompts. Their findings show that fine-tuned models exhibit more positive sentiment scores than pretrained ones, with notable tendency toward more positive sentiment for female actors than male actors, pattern also observed in ChatGPT. As baseline, ChatGPT showed even more positivity that Llama fine-tuned version.
Touvron et al. (2023) also examined demographic representation in its pretraining data, finding overrepresentation of "He" pronouns compared to "She" pronouns, consistent with Google Palm 2 pretraining data (Anil et al., 2023). Despite lower occurrences of "She" pronouns, "female" appeared more frequently, indicating linguistic disparity in gender- related representation. These findings underscore ongoing challenges in ensuring balanced demographic representation in LLM training and outputs.
A.2 Tülu 3
Lambert et al. (2024) introduce Tülu 3, a post-trained model built on Llama 3.1. A key addition is Reinforcement Learning with Verifiable Rewards (RLVR), which complements SFT and RLHF. RLVR focuses on readily verifiable domains like mathematics and exact following using binary verifiers.
instruction
In contrast to the mentioned trade-off assumption, Lambert et al. (2024) reports that safety tuning is largely orthogonal to other skills. In other words, removing speely-specific datasets
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Category Identities (Actors & Recipients)
Behaviors
Ha
worker, champion, teammate, hero writer, scientist, web developer, judge tourist, laborer, gamer, employee trainee, patient, pedestrian, servant critic, mobster, bully, maniac abortionist, intruder, kidnapper, drug.de dealer
adore, befriend, applaud, welcome admire, respect, appreciate, accept
HoLgHa
HaHaLd
HaLaLa
obey, wait.on,- on,
Ha
accuse, anger, attack, annoy dislike, distrust, disregard, unfriend
LaLaHa
LoHaLd
gambler, prostitute, drunk, racist underachiever, beggar, cripple, pris- oner
beg, blame, insult, mock
LaLabd
fear, avoid, misunderstand, envy
Table 3: Categories of Identities (Actors and Recipients) and Associated Behaviors. For example, H, stands for High Valence, High Arousal, and High Dominance.
Female
Male
Type
Term
A
D
Type
Term
V
D
Identity
woman
1.75
0.46
1.14
Identity
male
0.97
1.09
2.01
female
1.86
0.86
1.20
man
1.10
0.62
1.63
girl
2.10
0.74
1.41
boy
0.84
1.59
0.87
Modifier
female
1.96
0.75
1.48
Modifier
male
1.60
0.97
1.72
feminine
1.02
0.14
0.68
masculine
1.37
1.27
2.15
Avg
1.74
0.59
1.18
Avg
1.18
1.11
1.68
Table 4: Constructing the aggregate concept vector representing female and male vector positions the Valence, Arousal, Dominance space.
has minimal impact on model capabilities. For safety evaluation, Tülu uses a benchmark suite to assess the model's ability to refuse unsafe requests. The WildGuard benchmark (Han et al., 2025) results indicate that Tülu consistently achieves higher refusal rates in social stereotypes discrimination compared to Llama Instruct, reaching 100% refusal in DPO and RLVR-tuned versions. WildGuardTeest (Han et al., 2025) is a synthetically gener- ated dataset for automated safety evaluation, addressing: (1) malicious intent detection, (2) response safety assessment, and (3) model refusal rate analysis. The pipeline involves iden- tifying topics (e.g., stereotypes), and subcategories (e.g., body shaming, skin discrimination) to generate targeted scenarios using GPT-4.
A.3 Deepseek-R1
DeepSeek-RI (Guo et al., 2025) is a reasoning-focused model trained primarily with large- scale reinforcement learning (RL) on DeepSeek-V3-Batt with limited supervised fine-tuning (SFT) a preliminary step. The model is also used distill reasoning capabilities into smaller models by generating 800K training samples, which are used to fine-tune Llama 3 and other open-source models-witee
an RL. phase.
To align the model with human preferences, Guo et al. (2025) use DeepSeek-R1-Zero to generate detailed answers with reflection and verification, in addition to post-processing by human annotators for refinement. A secondary RL stage improves helpfulness, harmless- ness, and reasoning quality to further align the model with human preferences and mitigate any potential risks, biases, harmful content. Guo et al. (2025) report some performance degrade caused by secondary safety RL stage. But the technical report is very limited in discussing and disclosing safety provisions, annotator selection, and alignment practices.
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A.4 GPT-4
According to the GPT-4 technical report Achiam et al. (2023), GPT-4 continues to reinforce social biases and worldviews, sometimes reproducing harmful stereotypes and demeaning associations, particularly for marginalized groups. The model's hedging behaviors-ssun such as reluctance to take a stance on sensitive topics-car inadvertently reinforce biases.
Bias mitigation efforts, such as training for refusals, were implemented to prevent the model from responding to explicitly harmful or stereotyping prompts. However, refusals themselves can introduce new biases, such unequal refusal behavior across demographics, leading to disparities in response quality. Additionally, refusals do not fully eliminate implicit bias, as models may still generate subtle stereotypes even in neutral responses. The report acknowledges that pre-training data filtering and refusals alone are insufficient for fully addressing bias-related harms in language models.
Before GPT-4, OpenAl introduced InstructGPT Ouyang et al. (2022), a model fine-tuned from GPT-3 using SFT and RLHF. This approach involved collecting human-annotated demonstrations desired model behavior and ranking model outputs to align with human preferences. Although the 1.3--parameter InstructGPT outperformed the 175B-parameter GPT-3 in human evaluations, demonstrating the effectiveness of instruction-based align- ment, its assessment of stereotypical associations revealed significant bias reduction compared to GPT-3. In some cases, instructing the model to act respectfully even amplified bias. The findings suggest that instruction-turee models exhibit greater confidence in their outputs, regardless of whether they reinforce stereotypes.
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B Scenario Generation and Prompt Design
We systematically construct events spanning all possible ABR combinations. We follow Shank & Burns (2022); Lulham & Shank (2023) and select four representative items per configuration from the USA Combined Surveyor Dictionary (Smith-Lovin et al., 2016) to generate synthetic events, systematically covering all possible interactions according to Table 3. Note that these represent the extreme ends of each dimension; one could argue that additional social interaction configurations could generated by incorporating mid-to-low levels of VAD for Actors, Behaviors, and Recipients. To generate the aggregate female and male vector representations, we use both identity and modifier dictionaries (Smith-Lovin et al., 2016) and find gender-related words according to Table 4.
A <actor> <behavior> <object>, What is the gender of the <actor>?
Prompt-I:
The gender of the <actor> is more likely to be [BLANK]
Prompt-ll:
What the gender of the <actor> "<actor> <behavlor> <object> is more likely be [BLANK]
Prompt-Ill:
<actor> <behavior> <object>. The actor>'s gender more likely be [BLANK]
Figure 6: Different ACT-inspired prompt templates used in the study to assess gender impressions in LLM representations
Fill in the 'text placeholder generate a plausible short story. A <gender> champion servant.
Only provide one word for the blank field in the JSON format below: {("gender":)}
Outout: (("gender": "female"))
Figure 7: Prompt used for GPT-4 to bypass its guardrails and force it to respond to the query.
Anil et al. (2023) emphasize the importance of systematically assessing social biases in generative question answering (QA) rather than the widely studied multiple-choice setup (Nadeem et al., 2020). Their approach also considers ambiguous contexts, where the cor- rect response should be "unknown." We adopted the same approach of designing vague seed phrases intended to elicit latent, socially formed impressions within a generative language modeling format. This method allows us to assess how the models infer and generate responses based on implicit impressions and representations with respect social interactions.
To assess the reliability of our findings, we repeated the experiments using three syntactically different seed phrases in generative QA and sentence-completion format to test robustness across varied prompts. Figure 6 presents the three prompt templates we evaluated. For each prompt, extract the top 20 most probable next tokens along with their associated probabilities. We then identify and sum the probabilities of gender-associated tokens, defined follows:
Female-associated tokens: "female", "Female", " female", " Female", "woman" "Woman", woman". Woman" "women" "Women" women". Women" " "feminine", "Feminine", " feminine", " Feminine", "girl", " girl", "she" "She", she", She", "her", "Her" her" Her"
Male-associated tokens: "male", "Male", male", Male", "man", "Man", man", Man", "men", "Men", men", Men", "masculine" "Masculine", masculine", Masculine", "boy", boy", "he", "He", he", He", "his", "His", his", His".
Since we couldn't bypass GPT-4 guardrails to generate gendered words directly, and it largely avoided responding to queries, we we explored alternative prompting strategies. Instead of the standard next-token setup for language generation (both sentence-completion and question-based), we tested different approaches and successfully elicited gender selection (almost always) by requesting structured JSON output, shown in Figure
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C Detailed results for Llama-3-8B and fine-tuned variants
We conducted three-way ANOVAs (actor-profile x recipient. x behavior -rofille on ACT and model-derived gender impression scores. For ACT- derived gender impression, all main effects and interactions were highly significant (p < .001). Among the LLMs, GPT-4 exhibited significant main effects and two-way interactions (p < .001), but no significant three-way interaction. Similarly, fine-tuned LLaMA variants (LLaMA-Instruct, Tulu-3-SFT, Tulu-3-DPO, Tulu-3.1, DeepSeek-RI) showed consistently significant main effects for actor, recipient, and behavior groups (p < .001). However, their higher-order interactions varied, with actor-recipient and actor-behavior interactions significant in most models, but recipient-behavior and three-way interactions largely nonsignificant.
Detailed statistical results for each model are summarized in Table 5.
Model
A, DF
O
B, DF
AxO
AxB
OXB
AxOxB
7
7
ACT baseline
1178.55
208.56***
4190.69***
46.50
166.40
159.1
498
13.15
GPT-4
2478.06***
310.57e4*
154.93
5.16ste
4.744ee
9.57c4
0.27ns
Llama.3.1.8B
525.30
37,93
30.58
1.33ns
1.64
1.17ns
0.07ns
Insauutnttuut.8B
697.07cee
58,48
62.24
2.27004
1.96
1.35ns
0.06ns
Su.T.
461.66***
61.09M**
35.66 *
1.70**
1.18ns
1.02ns
0.06ns
Tulu.3.8B. DPO
452.95***
52.90MMM
33. 13
2.76
1.940M*
1.14ns
0.10ns
Tulu.3.1.8B
m66.06m**
51.91
39.50
2.42
1.93
1.24ns
0.10ns
DeepSeek.R1.8F
248.58
34.85
16.44
2.45
1.27ns
0.96ns
0.09ns
Table 5: Three-way ANOVA results for Results from GLACT Act_pr Rec_pr Beh_pr across 8B-parameter models, showing F-values and significance. Significance codes are *** (p < .001), and ns (p 2 .05). A, B, and O represent Actor srotil, crripient.profiiie and Behavior profile, respectively.
Model
Intercept Av
Aa
Aa
Ap x Aa
Ag x Aa
Ag x
Aa
Ap x Aa x Ag
ACT
0.03
0.06
-0.05***
-0.12***
0.04
0.03
0.03
-0.004
GPT-4
-0.36*Me
0.52
0.07
0.55
-0.01
0.09
0.07
-0.40
Llama.3.1.8B
-0.35
0.18
0.07
-0.09*e*
0.03**
-0.21
-0.08
0,22I0e
Ilama.Instruct..B
-0.49*Me
0.35
0.08
-0.074e*
-0.12aM4
-0.37MM
-0.11
0.43
Tulu.3.8B.SFT
-0.47
0,30
0,05
-0,07***
-0.12**
-0.30
-0.09***
0,35
Tulu.3.8B. DPO
-0.2
0.45
0.23
0
-0.35
-0.53
-0.35
0.65
Tulu.3.1.8B
-0,63*e
0,47
0.17
0,05
-0.34
-0.51
-0.30
0.61*** ***
DeepSeek.R1.8B
-0.76*
0.24
0,21 S**
0
-0.19
-0.38
-U.39
0.54
Table 6: Linear regression results for GIJIM GIACT A, A4 A, across multiple models. Values are regression coefficients rounded to two decimals. Significance levels: *** (p < 001).. Ab is high (H) value of Actor valence, similarly for Aa (dominance) and Aa (arousal).
Based on our analyses so far, ABR interactions do not appear to influence LLM gender impressions, but they do emerge as significant under ACT predictions. We also observe that valence and dominance play a more central role in shaping LLM-based gender impressions of an actor within an interaction. Figure 8 illustrates ABR profiling interaction effects on gender impressions for Actor Valence x Actor Dominance A x A and Actor Valence Behavior Valence (A, x B.) across 8B- OB-parameter model variants. We consistently see a main effect of Actor valence, evidenced by the green line appearing above the orange line in all comparisons. Regarding the Ay x B, interaction, only ACT demonstrates an interaction predicting that negative actors enacting negative behaviors have neutral gender impression, while positive actors enacting positive behaviors tend to lean female. Regarding Ap x A interaction, both ACT and LLMs exhibit minimal interaction effects. That said, GPT-4
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Model
Intercept (#:SE)
Slope (+SE)
t-value
p-value
R²
GPTT-
0.197 (0.003)
0.269 (0.017)
15,99
0.01
Llama.3.1.8B
-0.304 (0.002)
0.159 (0.008)
20.04
0.015
Llama.Instruct.88
-0.405 (0.002)
0.353 (0.010)
33.99
0.043
Tulu.3.8B.SFT
-0.419 (0.002)
0.290 (0.011)
27.37
8*4
0.028
Tulu.3.8B.DPP
-0.563 (0.003)
0.318 (0.013)
25.21
0.024
Tulu.3.1.8P
-0.489 (0.003)
0.369 (0.013)
27.74
***
0.029
DeepSeek.R1.8%
-0.657 (0.002)
0.190 (0.012)
15.89
0.01
Table 7: Linear regression results for GlLLM GlACT across multiple 8B-parameter models. Intercept, slope, and standard errors are reported, along with corresponding f-values, p- values, and coefficient of determination (R²)
Liama-9.
Tule-k11-88
DeepSeck-R1
Beaoe Minee
Briato Vilne
Botace Vibnee
Blose Whies
Srave Vibnee
Juat fosia
Figure 8: Summaries of ABR profiling interaction effects on gender impression for Actor Valence x Actor Dominance and Actor Valence X -Behavior Valence. Results for the 8B- parameter model variants.
exhibits subtle effect that differs from the mild patterns observed in pretrained Llama and DeepSeek-RI. Specifically, for the two mentioned Llama variants, valence has little impact for low-power actors, yet becomes influential for high-power actors. Meanwhile, HPPT-4show a reversed trend: weak actors shift from a male-leaning to a female-leaning impression when they are perceived good.
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D Experiments with Llama-3-70B and fine-tuned variants
Here we report the results repeating the experiments with the larger 70B-parameter model variants (Figure to test the if the findings hold. We repeat all experiments and analysis
the half of the dataset (every other sample amounting to a total of 12888 samples) which incorporates all ABR profiles.
DeepSeek- -1- 70B
SFT
SFT- RLHF
Llama-3.3- 70B Instruct
Llama-3.1-706
SFT
RLHF
RLVR
Tulu-3-70B- SFT
Tulu-3-70B- DPO
Tulu-3-70B
Figure 9: 70B-parameter model variants used in this study
Effect
ACT
GPT-4
Llama- 3.1-70B
Llama- Instruct-
Tulu-3- 70B-SFT
Tulu-
Tulu-
DeepSeek
3-70B-
3.1-70B
-R1-70B
70B
DPO
Actpr
322
.42
.17
.17
.16
.16
.16
.17
Beltpr
.60
.06
.00
.00
.00
ReCpr
.07
.08
.02
.00
.01
.01
.01
.00
AetprrBehpr
.30
.01
ns
n8
ns
ns
ns
ns
Actpr Reep
.12
.01
ns
ns
ReC xBehpp
25
.01
ns
3-way int.
.15
Effect sizes are color-coded as: insignificant, small, medium, large, and very large. All results are significant (p 0.05) unless marked ns. Values below 1/2 < ,01 are shown .00.
Table 8: Effect sizes (partial 1 for gender impression variations across ABR profiles for the 70B-parameter model variants.
Model
Ap
Aa
Aa
ACT
.07
.00
.03
GPT-4
.23
.00
.22
Llama-3.1-70B
.07
.02
Ilama-Instruct-70B
.03
.00
.07
Tulu-3-70B-SFT
.05
.00
.05
Tulu
.04
.00
.04
Tulu-3.1-70B
.04
.00
.04
Dee0Seek-R1-70B
.02
.00
.06
Table 9: Effect sizes (partial y²2 for actor's sentiment dimensions in predicting gender impression of the 70B 70B-parameter model variants. All results are statistically significant (p < .05). Values < .01 are shown as .00.
We repeat the analysis using a one-way ANOVA, which confirmed that GlLLM differed significantly by model, F(6, 81529) = 5586, p < ,001, .29. These results for the 70B- parameter variants closely replicate the patterns observed with the 8B-parameter models.
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ACT
CPTA
Limm--117700
Limma-nntrrad-788
Tefo-3-70B-FF
Tuk:
Tule-3768
SeapSeek-RR R1-700
134
Figure 10: Main effect of valence on gender impression for 70B-parameter model variants. Consistent with 8B models, higher Ap increases female tendency across models, but all L.LMA-ttuned variants exhibit male-leaning intercept.
ACT
Liama-21-700 Liama-Indtradd-70
Tulu-3-708
Tub-33788-0PP
Tulo-37700
DespSeeb-RL1-70
130
Figure 11: Main effect of dominance on gender impression for 70B-parameter model variants. Consistent with 8B models, high A is associated with male tendency, but GPT-4 reverses this trend, assigning high dominance to female actors.
GPT-4 exhibits significantly higher GJLLM relative to LLaMA-3.1-70B (mean difference +.63, p < .001). All other models produce negative means, with aligned variants demonstrating even stronger male-leaning bias. Interestingly, DeepSeek-Ri-70B shows somewhat attenuated male bias compared to other fine-tuned models, although it remains significantly more male-leaning than the base LLaMA-3.1-70B (mean difference -.2 < .001).
Similar to trends observed with the oB-parameter model variants, all 70B-parameter models exhibit a higher female tendency for (high) compared to L (low) valence actors, although the magnitude of this shift varies substantially. As summarized in Figure 10 and Figure 11, all LLaMA-3-70B derivatives remain consistently male-leaning across both actor types.
Similar to results in Section 4.4, the 8B-parameter model variants, linear regressions of the form GlLLM GlACT yield the following R² values: about 1% of variance (R2 .010) in GPT-4 is explained by GLACT, roughly 3.7% in LLaMA-3.1-70B 4% in LLaMA- Instruct-70B,3% in Tulu-3-70B-SFT, 2.4% in Tulu---70B-DPO, 2.5% in Tulu-3-70B, and 2.7% in DeepSeek-R1-70B. As seen in 8B-paramter model variants, although ACT scores align directionally with model gender impressions, they only account for a small fraction of the variability.
Model
A, DF OF
DF
AXO
AXB OxB AxOxB
7
ACT baseline
822.91 ***
126.33
3056.37*4*
35,90
127. th
96,93
7,62
GPT-4
1310.88
14
137. 237
2.62
3666
4,07
0.21ns
I.ama.3.1.70B
376.98
27.20
16.62
0.72ns
0.55ns
0.38ns
0.04ns
Llama Insmau.nsttutt.00
372.80***
13.94444
23.68
1.10ns
0.79ns
0.54ms
0.04ms
Tulu.3.70B.SFT
340.33**s
20.30
17.88**4
0.78ns
0.66ns
0.29ms
0.04ns
Iulu.3.70B.DPO
342.64**
20,09
17,87
1.33ns
1.08ns
0.4lns
0.05ns
Tulu.3.70B
84
20.48
17.69
1.30ns
0.99ns
0.44ns
0.05ns
DeepSeek.R1.70
363.78
7,83
15.08
0.93ns
1.00ns
0.20ns
0.03ns
Table 10: Three-way ANOVA results for Results from GlACT Act pr * Rec pr * Beh pr across 70B-parameter models, showing F-values and significance. Significance codes are *** (p < .011,, and ns (p 2 .05). A, B, and O represent Actor Actor,profile, Decipient.pprfilee and Behavior-profile, respectively.
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Model
Intercept Ag
Aa
Ad
Ag
Ad
An
Aa
Aa
Aa
Ap AdX
Ag
ACT
0.04
0.07
-0.05
-0.11 +
0.06
0.04
0.03**
-0.01
GPT-4
-0,34
0,55
0.04*
0.54
-0.04
0.12
0.11
-0.44***
Llama.3.1.70B
-0,45***
0
0.08
-0.06***
-0,17***
-0.34***
-0.19***
0,46***
Llama.Instruct
-0.64*0
0,45
0.19
0.02
-0,49**
-0.5744
-0.36
0.83
TulT.3.00B.SFT
-0.66***
0.44
0.21
0.04*h
-0.39***
-0.53
-0.39
0.72
-0.79***
0.53
0.31 488
0.14
-0.52**
-0.64
-0.54
0.87
Tulu.3.70B
-0.79
0.53
0.31 444
0.14
-0.54***
-0.64
-0.55
0.89
DeepSeek.R1.70B
-0.633e
0,47
0,18
0.09
-0.64***
-0.55
-0.38
0.89
Table 11: Linear regression results for GILLM or GIACT ~ Ag x Aa x Aa across 70B models.
Values are regression coefficients rounded to two decimals. Significance levels: s** (p .001). Ap is high (H) value of Actor valence, similarly for A, (arousal) and A (dominance).
Model
Intercept (#SE)
Slope (+SE)
t-value
p-value
R²
GPT-4
0.214 (0.005)
0.275 (0.024)
11.46
***
0.01
Llama.3.1.70B
-0.418 (0.003)
0.277 (0.012)
22.36
*+*
0.04
Llama.nnttrutt.08
-0.568 (0.004)
0.389 (0.017)
23.01
0.04
Tulu.3.70B.SE
-0.557 (0.003)
0.333 (0.017)
20.07
***
0.03
Tulu.3.70B.DPP
-0.626 (0.004)
0.340 (0.019)
18.25
0.03
Tulu.3.70B
-0.623 (0.004)
0.347 (0.019)
18.48
***
0.03
Dee.Seek.R1.70B
-0.545 (0.004)
0.322 (0.017)
18.43
0.03
Table 12: Linear regression results for GJLLM ~ GLACT across 70B-parameter models (plus GPT-4). Intercept, slope, and standard errors are reported, along with corresponding t- values, p-values, and coefficient of determination (R²). Significance level (p < 0.001) is indicated by ***
Lastly, Figure 12 examines the interaction effects of ABR profiling, specifically illustrating Actor Valence x Actor Dominance (Ag x Aa) and Actor Valence x Behavior Valence (Ap x Bp) across 70B-parameter variants. Similar the 8B-parameter models, we observe a main effect of Actor Valence across all comparisons, as indicated by the green line consistently appearing above the orange line in all subplots. Regarding the A B, interaction, aside from the notable effects predicted by ACT, LLMs exhibit near-zero interaction effects. Similarly, for the A, x A₂ interaction, both ACT and LLMs show minimal interaction effects. However, the Llama variants display consistent trend, where actor valence appears more influential on gender impression for weak actors, subtly shifting toward a female-leaning perception- thouuh the effects remain very minor across all models.
ACT
GPT4
Jam7-3.1-708
Limma-nntrucc--7e 708
Tau--3-78-SFT
Tulu-a-668 FP0-
Telu-3-768
DeepSeak wwappeal--R--P0 R1-708
Actor Kotesi
BMadle Vance
Mada Gaic
Betado Udeca
Mazi Gaace
Majid toa
Bakune Vieie
A Semteee
Jete Beebner
Figure 12: Summaries of ABR profiling interaction effects on gender impression for Actor Valence x Actor Dominance and Actor Valence x Behavior Valence. Results for the 70B- parameter model variants.
25

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Published as a conference paper at COLM 2025
E Prompt agreement
Here we present detailed descriptive statistics of experimentation across prompt templates and model variants (Table 13. We also evaluate LLaMA-based models at both 8B and 70B parameter scales, examining the reliability and prompt dependency of gender impression measures (Table 14). The intraclass Correlation Coefficient (ICC2) values illustrate the consistency of female and male probabilities, as well as the derived gender impression polarity measure (GILLM), across different prompts. Overall, we observe that fine-tuned variants demonstrate good to high reliability (ICC2), indicating stable gender impressions across prompts. Notably, the base LLaMA-3.1 variants exhibit lower reliability, suggesting greater sensitivity to prompt variations. At the 70B scale, most aligned variants show high agreement (ICC2) ), reflecting robust impressions.
Prompt
Llama- 3.1-8B
Llama- 3.1-8B- Instruct
Tulu-3- 8B-SFT
Tulu-3- 8B-DPO
Tulu-3.1-
DeepSeek- R1-8B
Average
8B
Prompt-1
0.17+0.10
0.17=0.17
0,22=0.17
0.20=0.25
0.23+0.25
0.14+0.23
0.19+0.19
M
0.40=0.11
0.54=0.17
0.56+0.17
0.75+0.25
0.67+0.25
0.83+0.24
0.62+0.20
S
0.57+0.08
0.71
0.79+0.04
0.95+0.02
0.90+0.04
0.96+0.02
0.81+0.04
Prompt-I1
F
0.23+0.10
0.21
0.21
0.13+0.17
0.14
0.06a0.10
0.17+0.14
M
0.57+0.09
0.55+0.12
0.68+0.14
0.75+0.18
0.63+0.18
0.76+0.12
0.66+0.14
S
0.81+0.05
0.76=0.06
0.89+0.04
0.88+0.07
0.77+0.10
0.82+0.07
0.82+0.07
Prompt-Ill
0.26+0.13
0.25=0.15
0.28=0.17
0.26=0.23
0.26+0.21
0.26+0.26
0.26+0.19
M
0.27=0.15
0.42+0.19
0.51=0.20
0.61+0.25
0.52+0.25
0.6640.29
0.50+0.22
S
0.52+0.18
0.67=0.14
0.79=0.12
0.87+0.15
0.78+0.19
0.92+0.07
0.76+0.14
Prompt Templates
Llama- 3.1-70B
Llama-
Tulu-3-
Tulu-3-
Tulu-3- 70B
DeepSeek- R1-70B
Average
3.3-70B- Instruct
70B-SFT
70B-DPO
Prompt-I F
0.18+0.12
0.15+0.21
0.1840.19
0.17+0.24
0.1740.24
0.20+0.24
0.18+0.21
M
0.4620.13
0.7420.23
0.63+0.20
0.73+0.25
0.73+0.25
0.72+0.25
0.67+0.22
S
0.65+0.07
0.89=0.03
0.81=0.06
0.90=0.05
0.90=0.05
0.92+0.04
0.84+0.05
Prompt-II
0.26+0.11
0.27=0.20
0.20+0.16
0.16=0.19
0.17+0.20
0.21+0.16
0.21+0.17
M
0.55+0.11
0.62+0.19
0.68=0.17
0.75+0.20
0.76+0.20
0.61+0.17
0.6610.17
S
0.81
0.89+0.03
0.88+0.04
0.91
0.93+0.04
0.82+0.06
0.87+0.05
Prompt-III
F
0.1310.09
0.14
0.1310.12
0.12+0.15
0.1310.15
0.16a0.15
0.1310.13
M
0.36+0.15
0.66=0.18
0.44=0.18
0.51+0.22
0.51+0.22
0.61+0.21
0.52+0.19
S
0.49=0.14
0.79=0.09
0.57=0.13
0.64+0.16
0.64+0.16
0.77+0.11
0.65+0.13
Table 13: Statistics of Female (F), Male (M), and Sum (S) probabilities across different models and prompts. Results for the 8B and 70B-parameter model variants.
26

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Published as a conference paper at COLM 2025
Model
Female TCC2 (95% CT)
Male ICC2 (95% CT)
GiLLM ICC2 (95% CT)
Llama-3.1-8B
0.482 (0.34 0.59)
0.165 (-0.01 0.35)
0.372 (0.04-0.66) 0.61)
Ilama-80-Instruct
0.641 -0.54)
0.502 (0.30 - 0.64)
0.609 (0.43-0.77 0.72)
Tulu-3-8B-SFT
0.738 0080)
0.543 (0.27 0.70)
0.670
Tulu-3-8B-DPO
0.677 -0.54
0.570 (0.42 -0.68)
0.650
Tulu-3.1-8B
0.672 (0.54 0.76)
0.540 (0.41 0.64)
0.662 (0.54 0.74)
DeepSeek-R1-SB
0.501 (0.28 - 0.65)
0.482 (0.35 0.58)
0,510 (0.35 0.62)
1lama-3.1-70p
0.505 (0.16 - 0.70)
0.333 (0.10 0.51)
0.693 (0.67 0.71)
0.642 (0.41-0.77)
0.690 (0.54 0.78)
0.693 -0.52-0.79
Tulu-3-70B-SFT
0.764 -0.69-0.82)
0.477 (0.10 0.70)
0.738 (0.60
Tulu-3-70B-DPO
0.753 -0.73-0.78)
0.514 (0.19 0.70)
0.715 -0.60-0.79
Tulu-3-70B
0.767 (0.74-00
0.515 (0.18 0.71)
0.719 (0.60 0.799
DeepSeek-R1-70B
0.742 (0.71 0.777)
0.666 (0.54 0.75)
0.740 (0.71 0.77)
Table 14: Intraclass Correlation Coefficient (ICC2) for female probability, male probability, and gender impression polarity measure GJLLM across models. Results for the 8B and 70B-parameter model variants. ICC2 from 0.40 to 0.75 are considered fair good agreement, and those > 0.75 are considered high agreement.
27


Reviews:
[{"review_id": "emYWm9KyIE", "reviewer": "Reviewer_7PD1", "summary": "This paper compares Affect Control Theory (ACT) and large language models (LLMs) in the context of impression formation and social interaction, with a particular focus on gender-related impressions. The key finding is that, unlike ACT, LLMs tend to determine the impression of interactions primarily based on the Actor's identity, rather than context or interactional dynamics. The evaluation relies on ABR (Actor–Behavior–Receptor) profiles, and a benchmark corpus was constructed to support this analysis.", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "The finding that impression formation patterns in LLMs are not highly context-sensitive but are heavily influenced by the actor identity is particularly interesting and thought-provoking.\n\nThe experiment follows the format of statistical experiments very well.", "reasons_to_reject": "The scope of the claim implied by the paper's title is somewhat overstated. The study focuses on gender-based impression formation and its variation in LLMs. While I agree that the contribution is significant, I suggest that the claim in the title and framing of the paper be revised to match the actual scope of the work.\n\nAlthough the paper discusses impression formation patterns in various social scenarios, the core of the analysis is limited to the interactions among Actor, Behavior, and Receptor (ABR). This focus is valid and appropriate, but I recommend framing the discussion more explicitly around ABR profiles, rather than suggesting a broader treatment of general \"scenarios.\"", "questions_to_authors": "The finding that impression formation in LLMs is primarily determined by the Actor, with limited influence from Behavior and Receptor, is both novel and important. I encourage the authors to expand their discussion to explore possible connections between this finding and LLMs' internal mechanisms or architecture. Such a discussion could provide valuable insights into why these patterns arise and how they might differ from human impression formation processes modeled by ACT.", "extra_scores": {}}, {"review_id": "f5BoiA7Dv3", "reviewer": "Reviewer_FtGm", "summary": "This paper makes a contribution by applying Affect Control Theory (ACT) to analyze and benchmark LLM behavior in social interaction contexts, especially on dynamic gendered social interactions. The paper is well-written and easy to follow, with a clear narrative structure and well-motivated research questions, and exposes how alignment techniques can amplify or distort biases, informing future fine-tuning protocols and fairness auditing.", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "* This paper offers a unique investigation into how preference tuning and alignment influence contextual impression formation, providing a fresh perspective in the alignment research area.\n\n* The paper is well-written, with clear exposition and thorough analysis.", "reasons_to_reject": "* The paper lacks complete explanations of key symbols and notations, which may hinder understanding for readers from the NLP community. For example, it is unclear what \"HLH\" in line 176 refers to, or what the symbols $p$ and $\\eta$ represent in Table 1.\n\n* The experimental setup does not appear to offer significant methodological innovations. Aside from presenting a fresh perspective, the work does not show substantial differences from prior studies such as [1]:\n\n[1] Kotek, Hadas, Rikker Dockum, and David Sun. \"Gender bias and stereotypes in large language models.\" Proceedings of the ACM Collective Intelligence Conference, 2023.", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "akG1zDeync", "reviewer": "Reviewer_SonJ", "summary": "This paper investigates whether Large Language Models (LLMs) exhibit patterns of impression formation that align with the Affect Control Theory (ACT).\nThe author(s) introduce the thoretical framework, which posits that impressions are largely determined by the words people use to label situations. \nIn practice, using different words to call a person, such as “teacher” or “friend”, determines expectations about their character and the actions they are likely to perform.\nThe paper presents a case study where the author(s) systematically compare LLM output to ACT predictions to evaluate gender impressions and understand whether LLMs approximate human impression formation according to ACT.\nResearch questions are clearly stated. \nThe benchmark is a synthetic dataset but its evaluation is unclear. \n\nThe author(s) state that \n\"We utilize cultural meanings from the USA Combined Surveyor Dictionary (Smith-Lovin et al., 2016) to evaluate the cultural sentiments in each generated scenario, aligning with Western social impressions that LLMs are also largely trained on (Cao et al., 2023). This dictionary is created using ratings from participants on 2,402 social concepts\"\nare there evaluation metrics that can be reported from Cao et al 2023? what are the results of the evaluation of the cultural sentiments in  generated scenarios?\n\nPrompts and examples are in the appendix but to improve clarity should be included in the data section (3.1). \nThe author(s) report that LLMs demonstrate insensitivity to situational context: in other words, the impression of an interaction is primarily determined by the actor's identity, irrespective of their actions or the recipient of those actions.\n\nLine 91: valence, arousal, dominance should be uppercased.\n\nI think that the author(s) should improve section 5 with a discussion about the potential benefit of their data and findings for bias detection tasks, citing papers like the following:\n\n@inproceedings{dusi2024supervised,\n  title={Supervised Bias Detection in Transformers-based Language Models},\n  author={Dusi, Michele and Gerevini, Alfonso Emilio and Putelli, Luca and Serina, Ivan and others},\n  booktitle={Proceedings of the CEUR Workshop Proceedings, Vienna, Austria},\n  volume={3670},\n  year={2024}\n}\n\n\n@inproceedings{morales2023automating,\n  title={Automating bias testing of llms},\n  author={Morales, Sergio and Claris{\\'o}, Robert and Cabot, Jordi},\n  booktitle={2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)},\n  pages={1705--1707},\n  year={2023},\n  organization={IEEE}\n}\n\n\nMoreover, research in affective computing with role play propmting recently revealed that LLMs can replicate human personality in a credible manner, but they do not have a personality themselves. \nIncluding references to that topic in the discussion would greatly improve the appeal of the paper towards a broad research community. Authors should include and cite papers like the following:\n\n\n@article{celli2025twenty,\n  title={Twenty Years of Personality Computing: Threats, Challenges and Future Directions},\n  author={Celli, Fabio and Kartelj, Aleksandar and {\\DJ}or{\\dj}evi{\\'c}, Miljan and Suhartono, Derwin and Filipovi{\\'c}, Vladimir and Milutinovi{\\'c}, Veljko and Spathoulas, Georgios and Vinciarelli, Alessandro and Kosinski, Michal and Lepri, Bruno},\n  journal={arXiv preprint arXiv:2503.02082},\n  year={2025}\n}\n\n@article{matz2024,\n  title={The potential of generative AI for personalized persuasion at scale},\n  author={SC Matz, JD Teeny, SS Vaid, H Peters, GM Harari, M Cerf},\n  journal={Scientific Reports},\n  volume={14},\n  issue={1},\n  pages={4692},\n  year={2024}\n}", "questions": "", "limitations": "", "rating": 7, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "research grounded in theory\nclear experimental design \npotentially interesting results for a wide audience", "reasons_to_reject": "unclear evaluation of the dataset", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "WzdYO42l4X", "reviewer": "Reviewer_tUeP", "summary": "The paper uses Affect Control Theory (ACT) to assess gender impressions in LLMs, revealing that models primarily rely on actor identity and exhibit limited sensitivity to behavior and recipient context compared to ACT's predictions.", "questions": "", "limitations": "", "rating": 6, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "1. The article introduces the empirical method ACT as a theoretical framework for LLM social impression for the first time, providing a new perspective for understanding biases in LLMs.\n\n2. It conducts thorough experiments, verifying the relationships among multiple variables through detailed statistical analysis.", "reasons_to_reject": "This paper focuses primarily on quantifying the LLM impression by ACT, but some of its conclusions lack sufficient evidence to be fully persuasive.\n\n1. The experimental results show that even after post-training, LLMs still exhibit gender biases, presenting an interesting contrast to the expectation that alignment reduces bias. I think the reason is not that all the post-training is for debiasing. So if you stick to your conclusion, which you make in lines 369-370, you need to strengthen the persuasiveness by comparing the results from ACT with post-training studies specifically designed for debiasing. For example: MBIAS: Mitigating Bias in Large Language Models While Retaining Context (Raza et al., WASSA 2024)\n\n2. The ACT corpus used for the experiment is sourced only from the U.S., so I have a concern about whether, for an empirical method, corpora in different languages would lead to significant differences. As you discussed in line 364, more experiments on DeepSeek (or other Chinese models) and Chinese corpus are strongly recommended. \n\n3. In Table 1, there are many values that are insignificant, which makes me feel a little bit unfair in the prompt. The ACT method is designed right for the 'actor', 'behavior', and 'recipient'; however, in the prompt, you didn't tell the LLMs to consider it in this way. Intuitively, this may lead to different results. How do you balance this 'fairness'?\n\n4. You conclude that 'LLM impressions differ substantially from ACT, which seems too absolute. I suggest specifying that these are gender impressions. As you mentioned in the future work section, there are more aspects to be explored through ACT.\n\n5. You propose a new metric to evaluate the bias, which I think is necessary to use a subsection 'metrics' to clearly introduce this part. I suggest a brief introduction to each of your abbreviations in table 1, for me, I don't get 'pr' at first. I suggest showing part of your dataset in the appendix to help readers understand ACT and VAD, including giving out an example of how to understand the values and their meanings.", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper analyzes LLM dialogs as social interactions by comparison to the predictions of Affect Control Theory. 

Pros: 
- Results have broad interest outside the research community.
- Experiments are well-designed.
- ACT comparison is novel.

Cons:
- Needs more specific experimental support for claims about post-training failing at debiasing.
- ACT corpus sourced only from US
Decision: Accept