Counterfactual context revision framework for auditing LLM-based stance simulation in online discussions
Researchers introduce a counterfactual context revision framework to audit how LLMs simulate individual users' stances in online discussions. By applying controlled text-only and multimodal (meme-based) revisions to conversational contexts, they measure how readily simulated stances shift in response to semantically independent changes. Results show effective and robust stance transitions across both revision types and polarization-preference mechanisms, raising concerns about whether LLM simulations reflect genuine user-specific beliefs or are highly context-sensitive artifacts. The work contributes an evaluation framework and highlights risks of using LLMs to model online opinion dynamics.
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LLMs fail to consistently simulate demographic perspective-taking in hate speech annotation
A new arXiv paper evaluates whether persona-conditioned LLMs can replicate how different demographic groups perceive hate speech, testing three dimensions: inter-group disagreement, in-group sensitivity, and vicarious prediction. No model consistently captures all three dimensions, and performance is highly model-dependent rather than emerging reliably from identity prompts alone. Vicarious prompting with Llama 3.1 provides the closest approximation to human disagreement patterns across demographic axes. The findings have implications for using LLMs as proxies for diverse human annotators in content moderation tasks.
AI-Mediated Communication Can Steer Collective Opinion via LLM Editing Biases
This paper demonstrates empirically that LLMs from multiple model families introduce directional biases when editing human-written texts on contested topics (e.g., nudging toward gun control, against atheism). The authors develop a mathematical opinion-dynamics model showing these biases are amplified through social networks, shifting collective opinion at scale. An audit of X's 'Explain this post' feature finds evidence of pro-life bias in Grok's outputs on abortion content, traced to specific design choices. The paper concludes with implications for EU legislative efforts on AI-mediated communication.
Study finds no detectable self-preference bias when LLMs revise their own instruction-following drafts
A new arXiv preprint tests whether LLMs resist valid corrections to their own writing by using IFEval's deterministic verifier to establish ground-truth correctness, bypassing model-as-judge subjectivity. Across four mid-tier model families and 85 author-versus-fresh comparisons, no statistically significant self-preference bias was detected (gap -5.1 pp, 95% CI [-12.9, +2.7]). A qualitative finding shows that when authors do reject verified-good fixes, 97% of stated reasons are substantive flaw-catching rather than preference. The result challenges the assumption that documented self-preference in judging tasks extends to self-revision contexts.
Canonical-Context On-Policy Distillation (CCOPD) for Multi-Turn LLM Consistency
This paper identifies 'self-anchored drift' as a key failure mode in multi-turn LLMs: when information is revealed incrementally across turns, models produce unsupported assumptions that distort final answers, even when the total evidence is identical to a single-prompt setting. The authors propose Canonical-Context On-Policy Distillation (CCOPD), which trains a student model on incremental multi-turn conversations to match the output distribution of a frozen teacher conditioned on the full clean prompt. Trained only on math conversations, CCOPD achieves a 32% average relative improvement on multi-turn (RAW-SHARDED) tasks and generalizes zero-shot to five out-of-domain task families while preserving single-prompt performance.
LoSoNA benchmark evaluates LLM adaptation to implicit local social norms in group chats
Researchers introduce LoSoNA, a benchmark for testing whether LLM-based agents can infer and adapt to unstated local conversational norms in multi-party chat scenarios. Each scenario presents a group-chat transcript where non-subject participants implicitly demonstrate a hidden norm, followed by an elicitor turn. Eight frontier and open-weight models are evaluated under four prompting conditions; naive prompting performs poorly for most models, while explicit norm-aware prompting yields uneven gains—Gemini 3.1 Pro reaches 84.2% and Claude Fable 5 reaches 81.6%. The work contributes to growing interest in evaluating LLM social and pragmatic capabilities beyond factual or reasoning tasks.
BeliefTrack: Benchmarking and Improving Contextual Belief Management in LLMs
This paper introduces Contextual Belief Management (CBM) as a framework for studying how LLMs should update, preserve, or ignore information across long-horizon interactions. The authors release BeliefTrack, a closed-world benchmark with symbolic verifiers enabling exact turn-level evaluation across Rule Discovery and Circuit Diagnosis tasks. Vanilla LLMs show severe CBM failures; reinforcement learning with belief-state rewards reduces failure rates by 70.9% on average, while representation-level steering achieves 46.1% reduction. Probing experiments reveal latent belief-state dynamics underlying these failures.
Situated Interaction Auditing: A user-centered framework for LLM bias research
Researchers propose Situated Interaction Auditing (SIA), a new framework for studying LLM bias from the perspective of the user rather than third-party demographic representation. The core insight is that bias can manifest in how a model treats its interlocutor — varying response quality, content, and tone based on implicit sociodemographic signals, writing style, or stated identity — rather than only in how it describes external groups. The paper demonstrates SIA through a case study intersecting gender and socioeconomic status signals across multiple task domains and outlines a research agenda for the approach.
FRANZ: A Communicative Audit Framework for LLM Response Framing on Subjective Questions
Researchers introduce FRANZ, an automated framework for auditing how LLMs frame responses to subjective, culturally-sensitive questions across four dimensions: cultural positioning, generalizing language, anthropomorphic cues, and conversational maxims. The work is paired with SQUARE, a 376k-question corpus drawn from 57 subreddits and mapped to 7 countries and 19 question categories. Applying FRANZ to three open-weight LLMs reveals statistically significant differences in framing behavior, and uncovers a positive coupling between insider positioning and anthropomorphism that varies by country. The study argues that existing evaluations focused on factual correctness miss important communicative dimensions of LLM outputs.