Vulnerability of LLMs' Belief Systems? LLMs Belief Resistance Check Through Strategic Persuasive Conversation Interventions

ACL ARR 2026 January Submission7794 Authors

Keywords: Large Language Models, LLM Belief, Meta-cognition, Prompt Engineering, Fine-tuning, Adversarial Attacks

Abstract:

Large Language Models (LLMs) are increasingly employed in various question-answering tasks. However, recent studies showcase that LLMs are susceptible to persuasion and could adopt counterfactual beliefs.
We present a systematic evaluation of LLM susceptibility to persuasion under the \emph{Source--Message--Channel--Receiver} (SMCR) communication framework.
Across five mainstream Large Language Models (LLMs) and three domains (factual knowledge, medical QA, and social bias), we analyze how different persuasive strategies influence belief stability over multiple interaction turns.
We further examine whether meta-cognition prompting (i.e., eliciting self-reported confidence) affects resistance to persuasion.
Results show that smaller models exhibit extreme compliance, with over 80\% of belief changes occurring at the first persuasive turn (average end turn of 1.1--1.4).
Contrary to expectations, meta-cognition prompting \emph{increases} vulnerability by accelerating belief erosion rather than enhancing robustness.
Finally, we evaluate adversarial fine-tuning as a defense. While GPT-4o-mini achieves near-complete robustness (98.6\%) and Mistral~7B improves substantially (35.7\% $\rightarrow$ 79.3\%), Llama models remain highly susceptible ($<$14\%) even when fine-tuned on their own failure cases.
Together, these findings highlight substantial model-dependent limits of current robustness interventions and offer guidance for developing more trustworthy LLMs.

Paper Type: Long

Research Area: Interpretability and Analysis of Models for NLP

Research Area Keywords: robustness, adversarial attacks, training, calibration, uncertainty

Contribution Types: Model analysis & interpretability, Data analysis

Languages Studied: English

Submission Number: 7794