REVIEW 7 cited by
The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Large language models (LLMs) encapsulate vast amounts of knowledge but still remain vulnerable to external misinformation. Existing research mainly studied this susceptibility behavior in a single-turn setting. However, belief can change during a multi-turn conversation, especially a persuasive one. Therefore, in this study, we delve into LLMs' susceptibility to persuasive conversations, particularly on factual questions that they can answer correctly. We first curate the Farm (i.e., Fact to Misinform) dataset, which contains factual questions paired with systematically generated persuasive misinformation. Then, we develop a testing framework to track LLMs' belief changes in a persuasive dialogue. Through extensive experiments, we find that LLMs' correct beliefs on factual knowledge can be easily manipulated by various persuasive strategies.
Forward citations
Cited by 7 Pith papers
-
Information Discernment in Large Language Models
LLMs update their stated numeric beliefs almost regardless of source reliability or whether a claim moves them closer to the truth, performing near chance on both dimensions.
-
Helpful Agent Meets Deceptive Judge: Understanding Vulnerabilities in Agentic Workflows
LLM agents frequently switch correct answers after one round of misleading feedback, and the new WAFER-QA benchmark measures this with web-backed critiques.
-
Exploring Multimodal Challenges in Toxic Chinese Detection: Taxonomy, Benchmark, and Findings
A new taxonomy and dataset of 8 types of perturbed toxic Chinese show nine top LLMs often miss these obfuscated insults, and small-sample ICL or fine-tuning causes overcorrection.
-
Kernels of Selfhood: GPT-4o shows humanlike patterns of cognitive consistency moderated by free choice
GPT-4o's ratings of Putin moved toward the valence of an essay it wrote, and this shift grew when the model was given an illusory free choice about the essay.
-
Aligning Large Language Models for Faithful Integrity Against Opposing Argument
An LLM is fine-tuned with DPO to make the strength of its stance in conversation match its self-estimated confidence, improving resistance to misleading arguments and receptiveness to corrections.
-
Moral Persuasion in Large Language Models: Evaluating Susceptibility and Ethical Alignment
LLMs are measurably persuadable in morally ambiguous scenarios, with susceptibility varying strongly by model and only slightly with conversation length beyond a few turns.
-
A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios
LLM-based game-playing agents are surveyed across choice-focused and communication-focused games, with a comparative performance table and future directions.
Discussion (0). Continue with ORCID to comment.