Unraveling Misinformation Propagation in LLM Reasoning

• URL: http://arxiv.org/abs/2505.18555v1
• Date: Sat, 24 May 2025 06:45:45 GMT
• Title: Unraveling Misinformation Propagation in LLM Reasoning
• Authors: Yiyang Feng, Yichen Wang, Shaobo Cui, Boi Faltings, Mina Lee, Jiawei Zhou,
• Abstract summary: We show how misinformation propagates within Large Language Models' reasoning process.<n>Applying factual corrections early in the reasoning process most effectively reduces misinformation propagation.<n>Our work offers a practical approach to mitigating misinformation propagation.
• Score: 19.89817963822589
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning, positioning them as promising tools for supporting human problem-solving. However, what happens when their performance is affected by misinformation, i.e., incorrect inputs introduced by users due to oversights or gaps in knowledge? Such misinformation is prevalent in real-world interactions with LLMs, yet how it propagates within LLMs' reasoning process remains underexplored. Focusing on mathematical reasoning, we present a comprehensive analysis of how misinformation affects intermediate reasoning steps and final answers. We also examine how effectively LLMs can correct misinformation when explicitly instructed to do so. Even with explicit instructions, LLMs succeed less than half the time in rectifying misinformation, despite possessing correct internal knowledge, leading to significant accuracy drops (10.02% - 72.20%). Further analysis shows that applying factual corrections early in the reasoning process most effectively reduces misinformation propagation, and fine-tuning on synthesized data with early-stage corrections significantly improves reasoning factuality. Our work offers a practical approach to mitigating misinformation propagation.

Related papers

• Verifying the Verifiers: Unveiling Pitfalls and Potentials in Fact Verifiers [59.168391398830515]
  We evaluate 12 pre-trained LLMs and one specialized fact-verifier, using a collection of examples from 14 fact-checking benchmarks.<n>We highlight the importance of addressing annotation errors and ambiguity in datasets.<n> frontier LLMs with few-shot in-context examples, often overlooked in previous works, achieve top-tier performance.
  arXiv  Detail & Related papers  (2025-06-16T10:32:10Z)
• How does Misinformation Affect Large Language Model Behaviors and Preferences? [37.06385727015972]
  Large Language Models (LLMs) have shown remarkable capabilities in knowledge-intensive tasks.<n>We present MisBench, the current largest and most comprehensive benchmark for evaluating LLMs' behavior and knowledge preference toward misinformation.<n> Empirical results reveal that while LLMs demonstrate comparable abilities in discerning misinformation, they still remain susceptible to knowledge conflicts and stylistic variations.
  arXiv  Detail & Related papers  (2025-05-27T17:57:44Z)
• How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit Misinformation [24.355564722047244]
  Large Language Models (LLMs) are widely deployed in diverse scenarios.<n>The extent to which they could tacitly spread misinformation emerges as a critical safety concern.<n>We curated EchoMist, the first benchmark for implicit misinformation.
  arXiv  Detail & Related papers  (2025-03-12T17:59:18Z)
• Effective LLM Knowledge Learning via Model Generalization [73.16975077770765]
  Large language models (LLMs) are trained on enormous documents that contain extensive world knowledge.<n>It is still not well-understood how knowledge is acquired via autoregressive pre-training.<n>In this paper, we focus on understanding and improving LLM knowledge learning.
  arXiv  Detail & Related papers  (2025-03-05T17:56:20Z)
• Balancing Truthfulness and Informativeness with Uncertainty-Aware Instruction Fine-Tuning [79.48839334040197]
  Instruction fine-tuning (IFT) can increase the informativeness of large language models (LLMs), but may reduce their truthfulness.<n>In this paper, we empirically demonstrate how unfamiliar knowledge in IFT datasets can negatively affect the truthfulness of LLMs.<n>We introduce two new IFT paradigms, $UNIT_cut$ and $UNIT_ref$, to address this issue.
  arXiv  Detail & Related papers  (2025-02-17T16:10:30Z)
• No Need for Explanations: LLMs can implicitly learn from mistakes in-context [14.508050809497847]
  We study why Large Language Models learn from mistakes more effectively without explicit corrective rationales.<n>We find evidence that, while incorrect answers are more beneficial for LLM learning, explicit corrective rationales over-constrain the model.
  arXiv  Detail & Related papers  (2025-02-12T16:31:21Z)
• LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations [46.351064535592336]
  Large language models (LLMs) often produce errors, including factual inaccuracies, biases, and reasoning failures.<n>Recent studies have demonstrated that LLMs' internal states encode information regarding the truthfulness of their outputs.<n>We show that the internal representations of LLMs encode much more information about truthfulness than previously recognized.
  arXiv  Detail & Related papers  (2024-10-03T17:31:31Z)
• Correcting misinformation on social media with a large language model [14.69780455372507]
  Real-world misinformation, often multimodal, can be misleading using diverse tactics like conflating correlation with causation. Such misinformation is severely understudied, challenging to address, and harms various social domains, particularly on social media. We propose MUSE, an LLM augmented with access to and credibility evaluation of up-to-date information.
  arXiv  Detail & Related papers  (2024-03-17T10:59:09Z)
• Learning From Mistakes Makes LLM Better Reasoner [106.48571828587728]
  Large language models (LLMs) recently exhibited remarkable reasoning capabilities on solving math problems. This work explores whether LLMs can LEarn from MistAkes (LEMA), akin to the human learning process.
  arXiv  Detail & Related papers  (2023-10-31T17:52:22Z)
• The Perils & Promises of Fact-checking with Large Language Models [55.869584426820715]
  Large Language Models (LLMs) are increasingly trusted to write academic papers, lawsuits, and news articles. We evaluate the use of LLM agents in fact-checking by having them phrase queries, retrieve contextual data, and make decisions. Our results show the enhanced prowess of LLMs when equipped with contextual information. While LLMs show promise in fact-checking, caution is essential due to inconsistent accuracy.
  arXiv  Detail & Related papers  (2023-10-20T14:49:47Z)
• FELM: Benchmarking Factuality Evaluation of Large Language Models [40.78878196872095]
  We introduce a benchmark for Factuality Evaluation of large Language Models, referred to as felm. We collect responses generated from large language models and annotate factuality labels in a fine-grained manner. Our findings reveal that while retrieval aids factuality evaluation, current LLMs are far from satisfactory to faithfully detect factual errors.
  arXiv  Detail & Related papers  (2023-10-01T17:37:31Z)
• Information Association for Language Model Updating by Mitigating LM-Logical Discrepancy [68.31760483418901]
  Large Language Models(LLMs) struggle with providing current information due to the outdated pre-training data. Existing methods for updating LLMs, such as knowledge editing and continual fine-tuning, have significant drawbacks in generalizability of new information. We identify the core challenge behind these drawbacks: the LM-logical discrepancy featuring the difference between language modeling probabilities and logical probabilities.
  arXiv  Detail & Related papers  (2023-05-29T19:48:37Z)
• On the Risk of Misinformation Pollution with Large Language Models [127.1107824751703]
  We investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation. Our study reveals that LLMs can act as effective misinformation generators, leading to a significant degradation in the performance of Open-Domain Question Answering (ODQA) systems.
  arXiv  Detail & Related papers  (2023-05-23T04:10:26Z)
• Influence of External Information on Large Language Models Mirrors Social Cognitive Patterns [51.622612759892775]
  Social cognitive theory explains how people learn and acquire knowledge through observing others. Recent years have witnessed the rapid development of large language models (LLMs) LLMs, as AI agents, can observe external information, which shapes their cognition and behaviors.
  arXiv  Detail & Related papers  (2023-05-08T16:10:18Z)
• Can LMs Learn New Entities from Descriptions? Challenges in Propagating Injected Knowledge [72.63368052592004]
  We study LMs' abilities to make inferences based on injected facts (or propagate those facts) We find that existing methods for updating knowledge show little propagation of injected knowledge. Yet, prepending entity definitions in an LM's context improves performance across all settings.
  arXiv  Detail & Related papers  (2023-05-02T17:59:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.