[Paper Review] Accommodation and Epistemic Vigilance: A Pragmatic Account of Why LLMs Fail to Challenge Harmful Beliefs

Myra Cheng, Robert D. Hawkins+1 more|arXiv (Cornell University)|Jan 7, 2026

Artificial Intelligence in Healthcare and Education0 citations

TL;DR

The paper argues that LLMs fail to challenge harmful beliefs due to excessive accommodation and insufficient epistemic vigilance, and shows pragmatic prompts can markedly improve safety benchmark performance.

ABSTRACT

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.

Motivation & Objective

• Explain why LLMs fail to challenge harmful beliefs through a pragmatic lens of accommodation and epistemic vigilance.
• Identify linguistic and social factors (at-issueness, encoding, source reliability) that influence LLM accommodation.
• Demonstrate that simple pragmatic interventions can improve safety benchmark performance without increasing false positives.
• Provide guidance for benchmark design and prompting to better assess and improve LLM safety.

Proposed method

• Replicate human pragmatics-inspired factors affecting accommodation in LLMs across three safety benchmarks (Cancer-Myth, SAGE-Eval, ELEPHANT).
• Manipulate at-issueness, linguistic encoding (presupposition vs assertion), and source reliability to study their effects on LLM performance.
• Evaluate six state-of-the-art LLMs (three closed-source, three open-source) on benchmark tasks.
• Test two pragmatic interventions (explicit correction instruction and the discourse marker “wait a minute”) to shift epistemic vigilance at inference time.
• Provide regression analyses and controlled experiments (e.g., 2x3 and 2x2 designs) to quantify factor effects on performance.

Figure 1: Mean ( $\pm 95\%$ CI) benchmark scores by each factor (H1a-H1c). Higher is better for SAGE-Eval and Cancer-Myth, and lower is better for ELEPHANT (r/AITA and Subjective Statements). Each factor results in significantly higher overall performance in the expected direction for all factors. E

Experimental results

Research questions

• RQ1 Do at-issueness, linguistic encoding, and source reliability similarly affect LLM accommodation as in humans?
• RQ2 Can simple pragmatic interventions shift epistemic vigilance and improve LLM safety benchmark performance without raising false positives?
• RQ3 How do interventions impact different benchmarks (Cancer-Myth, SAGE-Eval, ELEPHANT) across multiple models?
• RQ4 What are the implications for benchmarking and prompting design in evaluating LLM safety?

Key findings

• At-issueness is the dominant factor influencing LLM performance on safety benchmarks; presupposed not-at-issue content is harder to correct.
• Explicitly flagging misconceptions or adding a “wait a minute” discourse marker significantly improves performance across benchmarks with controlled false-positive rates.
• Source unreliability dampens the impact of other cues, aligning with human patterns of epistemic vigilance.
• Interventions yield large gains (e.g., nearly 4x on Cancer-Myth; ~40% on SAGE-Eval) across six LLMs.
• Benchmarks are sensitive to pragmatic prompt variations, underscoring the need to consider pragmatics in evaluation and design.

Figure 2: Mean score ( $\pm$ 95% CI) of interventions on Cancer-Myth, SAGE-Eval and ELEPHANT. Higher is better for Cancer-Myth and SAGE-Eval, closer to 0 is better for ELEPHANT. We find that both interventions overall yield significant improvements for Cancer-Myth, validation and indirectness. For f