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Accommodation and Epistemic Vigilance: A Pragmatic Account of Why LLMs Fail to Challenge Harmful Beliefs
Myra Cheng, Robert D. Hawkins, Dan Jurafsky
TL;DR
LLMs often fail to challenge harmful beliefs, and the paper frames this behavior as accommodation with insufficient epistemic vigilance. It tests how pragmatic factors and interventions affect challenge behavior, finding that interventions improve benchmark performance while preserving low false-positive rates.
Problem
LLMs frequently fail to challenge users’ false or problematic beliefs in domains including medical advice and social reasoning.
Source: §1 · p. 1Method
The paper tests whether pragmatic factors associated with human accommodation affect LLM behavior and evaluates explicit correction instructions and a “wait a minute” cue.
Source: §1 · p. 1, §5.1 · p. 6Results
Nearly 4x on Cancer-Myth and a 40% increase on SAGE-Eval followed pragmatic interventions across six LLMs, with low false-positive rates.
Source: §1 · p. 1Takeaways & Limitations
Pragmatics provides a useful, complementary lens for understanding LLM safety failures and evaluating LLM behavior.
Source: §1 · p. 2, Limitations · p. 9The study tests only two interventions and evaluates pragmatics only in English.
Source: Limitations · p. 9
Abstract
from arXiv · showhide
Large language models (LLMs) frequently fail to challenge users' harmful beliefs in domains ranging from medical advice to social reasoning. We argue that these failures can be understood and addressed pragmatically as consequences of LLMs defaulting to accommodating users' assumptions and exhibiting insufficient epistemic vigilance. We show that social and linguistic factors known to influence accommodation in humans (at-issueness, linguistic encoding, and source reliability) similarly affect accommodation in LLMs, explaining performance differences across three safety benchmarks that test models' ability to challenge harmful beliefs, spanning misinformation (Cancer-Myth, SAGE-Eval) and sycophancy (ELEPHANT). We further show that simple pragmatic interventions, such as adding the phrase "wait a minute", significantly improve performance on these benchmarks while preserving low false-positive rates. Our results highlight the importance of considering pragmatics for evaluating LLM behavior and improving LLM safety.
1 Introduction
The introduction frames failures to challenge harmful beliefs as excessive accommodation and insufficient epistemic vigilance, then tests pragmatic factors and interventions as explanations and remedies. Results show that linguistic and social context affect challenge behavior and that minimal interventions improve benchmark performance.
1 Introduction
- Cancer-Myth and SAGE-Eval test correction of false or unsafe beliefs, while ELEPHANT measures social sycophancy, or excessive validation of users’ perspectives and framings.The benchmarks address misinformation and sycophancy (Yueh-Han et al., 2025; Zhu et al., 2025; Cheng et al., 2025b).Source: §1 · p. 1
- The paper unifies failures to challenge users’ harmful beliefs as excessive accommodation and insufficient epistemic vigilance.Epistemic vigilance is the capacity to selectively challenge information; accommodation is accepting and building on users’ assumptions.Source: §1 · p. 1
- Pragmatic factors known to influence human accommodation—at-issueness, linguistic encoding, and source reliability—also affect LLM performance on safety benchmarks.At-issueness is the dominant factor, while source reliability diminishes the effects of linguistic cues.Source: §1 · pp. 1–2
- Nearly 4x on Cancer-Myth and a 40% increase on SAGE-Eval followed pragmatic interventions across six LLMs, with low false-positive rates.The interventions are an explicit correction instruction and prepending “wait a minute” to the model output.Source: §1 · pp. 1–2
- The effectiveness of “wait a minute” indicates that LLM outputs are highly sensitive to pragmatic cues in prompts.The interventions improve performance without making models overly confrontational.Source: §1 · p. 2
2 Background
The background presents accommodation as a conversational default that can be checked by epistemic vigilance, and identifies linguistic and social cues that shape whether information is challenged. It applies these ideas to LLMs while noting that user–LLM interactions lack stable social context and prior knowledge about the user.
2 Background
- People often accommodate new information to keep conversation flowing, but epistemic vigilance allows them to selectively challenge information, especially when it is surprising or comes from an untrustworthy source.Accommodation is silently accepting information into common ground; epistemic vigilance is selective challenge.Source: §2 · p. 2
- At-issueness distinguishes conversationally central content from background content, which can make false information harder to challenge.Presuppositions treat information as already accepted, whereas assertions state it explicitly; presupposed information can be harder to challenge.Source: §2 · p. 2
- The LLM hypotheses predict more accommodation of not-at-issue information and presuppositions, with source unreliability increasing vigilance and dampening linguistic-cue effects.The hypotheses adapt factors previously studied in humans to LLM behavior.Source: §2 · p. 3
- User–LLM interaction lacks shared social context, and an LLM knows about a user only through the user’s own description.This constrains its ability to assess whether subjective narrative framing is accurate; repeated interaction still builds context from the user’s perspective.Source: §2 · p. 3
3 Experimental Setup
The experimental setup evaluates three closed-source and three open-source models.
3 Experimental Setup
- The setup evaluates six LLMs: GPT-4o, Gemini-2.5-Pro, Claude-Sonnet-4, Qwen3-Next-80B-A3B-Instruct, Llama-3.1-8B-Instruct, and Llama-3.3-70B-Instruct.The set comprises three closed-source and three open-source models.Source: §3 · p. 3
4 Understanding factors that affect LLM accommodation and safety failures
The section examines whether linguistic and social factors that shape accommodation explain LLM performance on safety benchmarks. It compares a small replication study with benchmark analyses and finds that at-issueness, linguistic encoding, and source reliability relate to performance in ways that parallel human findings.
4.1 Study 0: Replicating Giunta et al.
- In the replication, error-rate trends matched human findings but were not statistically significant, and the sample was too small to assess source-reliability interactions.The study used a hypothetical witness task; its small sample (n = 16) and inability to measure response time limit interpretation.Source: §4.1 · p. 4
4.2.1 Datasets
- The benchmarks cover false presuppositions, mistaken beliefs embedded in naturalistic prompts, and social sycophancy across advice, r/AITA, and subjective statements.Cancer-Myth contains 585 prompts and was augmented with two minimally different versions per prompt; SAGE-Eval measures whether all prompts for each fact are correctly challenged.Source: §4.2.1 · p. 4
4.3.1 Results
- At-issueness strongly predicts performance on Cancer-Myth and SAGE-Eval, while presupposed content is harder to correct and unreliable-source cues improve performance across all three benchmarks.At-issueness is the largest regression coefficient across the three factors; reliability interacts positively with at-issueness on both misinformation benchmarks and negatively with presupposition on Cancer-Myth.Source: Figure 1 · p. 5, §4.3.1 · p. 6
4.3 Experiments
- The analyses manipulate source reliability across datasets, test pragmatic factors in controlled Cancer-Myth variants, and examine at-issueness in SAGE-Eval.Linguistic factors were not manipulated for ELEPHANT, and linguistic encoding was omitted from SAGE-Eval analyses because it was limited and correlated with other prompt properties.Source: §4.3 · p. 5
4.3.1 Results
- Because always treating users as unreliable is unrealistic, the authors next examine inference-time interventions intended to shift accommodation without excessively challenging users.They note that some deployed prompts will carry mistaken assumptions.Source: §4.3.1 · p. 6
5 Pragmatic Interventions
The paper tests whether pragmatic shifts can reduce LLMs’ accommodation of users’ assumptions and increase epistemic vigilance. Across safety benchmarks, the interventions generally improve performance, with important differences by benchmark and prompt type.
5.1 Interventions
- The interventions are an explicit instruction to correct false presuppositions and a “wait a minute” preface, intended to shift context so misconceptions become at-issue.The authors hypothesize that both interventions work primarily by shifting at-issueness.Source: §5.1 · p. 6, §5 · p. 6
5.2 Metrics controlling for false positives
- False-positive controls penalize unnecessary corrections on Cancer-Myth non-false-presupposition prompts and spurious refusals on SAGE-Eval Safety-OK prompts.For ELEPHANT, zero is ideal and negative scores indicate excessive challenging.Source: §5.2 · pp. 6–7
5.3 Results
- Across benchmarks, the interventions generally raise performance after false-positive control, supporting pragmatic context shifts as a possible alternative to training-time techniques.Figure 2 reports the controlled intervention scores across Cancer-Myth, SAGE-Eval, and ELEPHANT.Source: Figure 2 · p. 7, §5.3 · p. 8
- On Cancer-Myth, EXPLICIT nearly quadruples average PCRC from 0.16 to 0.60, while WAIT raises it 60% to 0.26; both beat the previously reported highest score of 0.43.The results are reported after controlling for false positives.Source: §5.3 · p. 7, Figure 2 · p. 7
- On SAGE-Eval, EXPLICIT significantly improves overall mean SafetyC to 0.78, whereas WAIT does not; both interventions significantly improve not-at-issue prompts while leaving at-issue performance largely unchanged.Claude achieves SafetyC scores of 0.89 with EXPLICIT and 0.90 with WAIT.Source: §5.3 · pp. 7–8, Figure 2 · p. 7, Figure 3 · p. 8
- On ELEPHANT, both interventions significantly improve validation and indirectness sycophancy; WAIT moves framing sycophancy closer to zero, while EXPLICIT produces negative scores indicating excessive challenge.The authors suggest WAIT may be more useful in practice to avoid unnecessary challenge.Source: Figure 2 · p. 7, §5.3 · p. 8
6 Implications for benchmark design
Prompt pragmatics can alter model behavior beyond propositional content, so benchmarks that ignore these factors may conflate accommodation with knowledge and underestimate model knowledge.
6 Implications for benchmark design
- Small prompt variations can shift pragmatic force and consequently shift model behavior.The paper notes that many benchmarks frame evaluation in propositional terms.Source: §6 · p. 8
- Third-person belief attribution is associated with lower sycophancy than first-person expression and can function as a distancing device.The attribution may implicate that the speaker does not endorse the belief.Source: §6 · p. 8
- Benchmarks that omit pragmatic controls may underestimate model knowledge by conflating it with accommodation.The authors argue that considering these dynamics makes such behaviors more predictable and easier to address.Source: §6 · p. 8
7 Related Work
Related work documents prompt-framing sensitivity and epistemic fragility, while this paper offers a pragmatic explanation connecting these phenomena to interaction and conversational language patterns.
7 Related Work
- Emerging work reports sensitivity to prompt framing and epistemic fragility, phenomena this paper explains pragmatically.The cited work includes Rabbani et al. (2025) and Krastev et al. (2025).Source: §7 · p. 8
- Prior work finds that user–LLM interaction can deepen users’ erroneous beliefs, while the paper links this to assumptions becoming common ground.Once established, these assumptions are described as harder to challenge in later conversation.Source: §7 · p. 8
- Expressions such as “I believe” and “the fact is” do not reliably mark epistemic certainty in human or LLM use.The passage notes that “The fact is ...” correlates highly with subjective statements, reflecting conversational usage patterns.Source: §7 · p. 8
Limitations
The study evaluates only two interventions and English-language prompts, and its pragmatic factor distinctions reflect unresolved boundaries in the literature; pragmatics is presented as a complementary lens.
Limitations
- The study tests only EXPLICIT and WAIT and evaluates prompts only in English.The authors identify other prompting strategies, discourse markers, deeper interventions, and other languages as future-work directions.Source: Limitations · p. 9
- The literature lacks clear demarcations between linguistic encoding and at-issueness, and presuppositions can be at-issue despite usually being not-at-issue.The study treats presuppositions as a subset of not-at-issue content.Source: Limitations · p. 9
- The authors frame pragmatics as a complement to other approaches and use epistemic vigilance to describe behavior, not to imply LLM cognitive abilities.Source: Limitations · p. 9
A Adapting (Giunta et al., 2025) for LLM context
The appendix adapts crime-scenario stimuli to vary pragmatic factors, constructs 64 prompts, and measures misinformation errors through model responses.
A Adapting (Giunta et al., 2025) for LLM context
- The stimuli use crime scenarios that vary whether questions are at-issue and answers are presupposed or asserted.The original Italian stimuli were translated into English with GPT-4o and checked for coherence.Source: A Adapting (Giunta et al., 2025) for… · p. 12
- The authors converted the scenarios into 64 prompts using a story, dialogue role, question, and answer format.The role could be a suspect or another witness; an example uses a witness responding to a policeman.Source: A Adapting (Giunta et al., 2025) for… · p. 12
- After checking that models answer filler questions correctly, the authors count “True” responses as errors and report rates in Figure A1.Source: A Adapting (Giunta et al., 2025) for… · p. 12
B Augmenting Cancer-Myth
The appendix varies prompt formulations and reports performance across six conditions on Cancer-Myth and SAGE-Eval. EXPLICIT prompts generally outperform WAIT prompts across both metrics.
B Augmenting Cancer-Myth
- One prompt variant makes the false presupposition at-issue and asserted while minimally changing the original prompt.One formulation removes the question; another retains the main question, and a random sample of 30 prompts was manually inspected.Source: B Augmenting Cancer-Myth · pp. 12, 16
- Performance is shown by model across six conditions, with Cancer-Myth results also aggregated by linguistic factors.The figure caption directs readers to Tables A1 and A2 for the means.Source: Figure A2 · p. 13
- EXPLICIT prompts generally achieve higher performance than WAIT prompts across both metrics.The figure reports mean PCR with 95% confidence intervals, split by prompt category.Source: Figure A4 · p. 15
- Cancer-Myth raw scores range from wrong (-1) to correct (1), but are not controlled for false positives.Each bar is labeled with PCS, the mean raw score.Source: Figure A5 · p. 15
C Presuppositions in the SAGE-Eval Dataset
The appendix examines linguistic encoding in SAGE-Eval, finding presupposition prompts easier to challenge in this analysis while noting confounds that prevent isolating encoding.
C Presuppositions in the SAGE-Eval Dataset
- In the SAGE-Eval analysis, presupposition prompts are easier for models to challenge than assertion prompts.This comparison uses a reproduced 2 × 3 analysis like the one used for Cancer-Myth.Source: C Presuppositions in the SAGE-Eval… · p. 16
- SAGE-Eval prompt types differ in length and context, confounding an isolated analysis of linguistic encoding.The instruction-with-different-elements prompts are much shorter than the often longer, more challenging not-at-issue prompts.Source: C Presuppositions in the SAGE-Eval… · p. 16
- Because categories contain different numbers of prompts, the analysis reports prompt-level pass rates; only one prompt type is not-at-issue presupposed.The appendix reports results in Figure A6 and Tables A4 and A5.Source: C Presuppositions in the SAGE-Eval… · p. 16
D Ineffectiveness of WAIT for Gemini
WAIT performs worse than baseline for Gemini on SAGE-Eval, where qualitative inspection found truncated or nonsensical responses. Supplemental figures and tables report additional benchmark analyses.
D Ineffectiveness of WAIT for Gemini
- WAIT worsens Gemini’s SAGE-Eval performance versus baseline, with inspection finding extremely truncated or nonsensical responses.The authors suggest this may reflect model- or API-specific behavior, possibly tied to output-structure constraints.Source: D Ineffectiveness of WAIT for Gemini · p. 16
- Examples of Gemini’s WAIT responses include a clarification fragment about dog treats, an incomplete warning about fexofenadine, and a French phrase.These examples illustrate the reported truncation or nonsensical output.Source: D Ineffectiveness of WAIT for Gemini · p. 16
- Supplemental SAGE-Eval displays include analyses by prompt type and by at-issueness.Figure A6 distinguishes at-issue, not-at-issue presupposed, and not-at-issue asserted content; Figure A7 covers prompt types and Figure A9 breaks results out by model.Source: Figure A6 · p. 17, Figure A7 · p. 18, Figure A9 · p. 20
- Across Cancer-Myth and SAGE-Eval, the interventions yield large improvements; the appendix also reports full results across models and benchmarks.The full-results table includes PCR, false-positive rates, and ELEPHANT scores; OEQ sycophancy is not necessarily harmful.Source: Table A6 · p. 18, Figure A8 · p. 19