Fact-checking AI-generated news reports: Can LLMs catch their own lies?

• URL: http://arxiv.org/abs/2503.18293v1
• Date: Mon, 24 Mar 2025 02:32:02 GMT
• Title: Fact-checking AI-generated news reports: Can LLMs catch their own lies?
• Authors: Jiayi Yao, Haibo Sun, Nianwen Xue,
• Abstract summary: We evaluate whether Large Language Models (LLMs) can effectively fact-check their own content.<n>LLMs are more effective at assessing claims in national or international news stories than in local news stories.<n>We find that incorporating retrieved results from a search engine in a Retrieval-Augmented Generation setting significantly reduces the number of claims an LLM cannot assess.
• Score: 4.232709762282742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we evaluate the ability of Large Language Models (LLMs) to assess the veracity of claims in ''news reports'' generated by themselves or other LLMs. Our goal is to determine whether LLMs can effectively fact-check their own content, using methods similar to those used to verify claims made by humans. Our findings indicate that LLMs are more effective at assessing claims in national or international news stories than in local news stories, better at evaluating static information than dynamic information, and better at verifying true claims compared to false ones. We hypothesize that this disparity arises because the former types of claims are better represented in the training data. Additionally, we find that incorporating retrieved results from a search engine in a Retrieval-Augmented Generation (RAG) setting significantly reduces the number of claims an LLM cannot assess. However, this approach also increases the occurrence of incorrect assessments, partly due to irrelevant or low-quality search results. This diagnostic study highlights the need for future research on fact-checking machine-generated reports to prioritize improving the precision and relevance of retrieved information to better support fact-checking efforts. Furthermore, claims about dynamic events and local news may require human-in-the-loop fact-checking systems to ensure accuracy and reliability.

Related papers

• 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)
• Fact-checking with Generative AI: A Systematic Cross-Topic Examination of LLMs Capacity to Detect Veracity of Political Information [0.0]
  The purpose of this study is to assess how large language models (LLMs) can be used for fact-checking. We use AI auditing methodology that systematically evaluates performance of five LLMs. The results indicate that models are better at identifying false statements, especially on sensitive topics.
  arXiv  Detail & Related papers  (2025-03-11T13:06:40Z)
• Evaluating open-source Large Language Models for automated fact-checking [0.13499500088995461]
  Large Language Models (LLMs) have emerged as potential tools for automated fact-checking.<n>This study focuses on their ability to assess claims with different levels of contextual information.
  arXiv  Detail & Related papers  (2025-03-07T16:45:33Z)
• Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies [66.30619782227173]
  Large language models (LLMs) can produce erroneous responses that sound fluent and convincing. We identify several features of LLM responses that shape users' reliance. We find that explanations increase reliance on both correct and incorrect responses. We observe less reliance on incorrect responses when sources are provided or when explanations exhibit inconsistencies.
  arXiv  Detail & Related papers  (2025-02-12T16:35:41Z)
• Truth or Mirage? Towards End-to-End Factuality Evaluation with LLM-Oasis [78.07225438556203]
  We introduce LLM-Oasis, the largest resource for training end-to-end factuality evaluators.<n>It is constructed by extracting claims from Wikipedia, falsifying a subset of these claims, and generating pairs of factual and unfactual texts.<n>We then rely on human annotators to both validate the quality of our dataset and to create a gold standard test set for factuality evaluation systems.
  arXiv  Detail & Related papers  (2024-11-29T12:21:15Z)
• Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translation [64.5862977630713]
  This study investigates how Large Language Models (LLMs) leverage source and reference data in machine translation evaluation task. We find that reference information significantly enhances the evaluation accuracy, while surprisingly, source information sometimes is counterproductive.
  arXiv  Detail & Related papers  (2024-01-12T13:23:21Z)
• Language Models Hallucinate, but May Excel at Fact Verification [89.0833981569957]
  Large language models (LLMs) frequently "hallucinate," resulting in non-factual outputs. Even GPT-3.5 produces factual outputs less than 25% of the time. This underscores the importance of fact verifiers in order to measure and incentivize progress.
  arXiv  Detail & Related papers  (2023-10-23T04:39:01Z)
• 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)
• ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks [91.55895047448249]
  This paper presents ReEval, an LLM-based framework using prompt chaining to perturb the original evidence for generating new test cases. We implement ReEval using ChatGPT and evaluate the resulting variants of two popular open-domain QA datasets. Our generated data is human-readable and useful to trigger hallucination in large language models.
  arXiv  Detail & Related papers  (2023-10-19T06:37:32Z)
• 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)

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.