An Empirical Analysis of LLMs for Countering Misinformation

• URL: http://arxiv.org/abs/2503.01902v1
• Date: Fri, 28 Feb 2025 07:12:03 GMT
• Title: An Empirical Analysis of LLMs for Countering Misinformation
• Authors: Adiba Mahbub Proma, Neeley Pate, James Druckman, Gourab Ghoshal, Hangfeng He, Ehsan Hoque,
• Abstract summary: Large Language Models (LLMs) can amplify online misinformation, but show promise in tackling misinformation.<n>We empirically study the capabilities of three LLMs -- ChatGPT, Gemini, and Claude -- in countering political misinformation.<n>Our findings suggest that models struggle to ground their responses in real news sources, and tend to prefer citing left-leaning sources.
• Score: 4.832131829290864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While Large Language Models (LLMs) can amplify online misinformation, they also show promise in tackling misinformation. In this paper, we empirically study the capabilities of three LLMs -- ChatGPT, Gemini, and Claude -- in countering political misinformation. We implement a two-step, chain-of-thought prompting approach, where models first identify credible sources for a given claim and then generate persuasive responses. Our findings suggest that models struggle to ground their responses in real news sources, and tend to prefer citing left-leaning sources. We also observe varying degrees of response diversity among models. Our findings highlight concerns about using LLMs for fact-checking through only prompt-engineering, emphasizing the need for more robust guardrails. Our results have implications for both researchers and non-technical users.

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. The extent to which they could tacitly spread misinformation emerges as a critical safety concern. We curated ECHOMIST, the first benchmark for implicit misinformation.
  arXiv  Detail & Related papers  (2025-03-12T17:59:18Z)
• 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.<n>We identify several features of LLM responses that shape users' reliance.<n>We find that explanations increase reliance on both correct and incorrect responses.<n>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)
• Missci: Reconstructing Fallacies in Misrepresented Science [84.32990746227385]
  Health-related misinformation on social networks can lead to poor decision-making and real-world dangers. Missci is a novel argumentation theoretical model for fallacious reasoning. We present Missci as a dataset to test the critical reasoning abilities of large language models.
  arXiv  Detail & Related papers  (2024-06-05T12:11:10Z)
• Multimodal Large Language Models to Support Real-World Fact-Checking [80.41047725487645]
  Multimodal large language models (MLLMs) carry the potential to support humans in processing vast amounts of information. While MLLMs are already being used as a fact-checking tool, their abilities and limitations in this regard are understudied. We propose a framework for systematically assessing the capacity of current multimodal models to facilitate real-world fact-checking.
  arXiv  Detail & Related papers  (2024-03-06T11:32:41Z)
• What Evidence Do Language Models Find Convincing? [94.90663008214918]
  We build a dataset that pairs controversial queries with a series of real-world evidence documents that contain different facts. We use this dataset to perform sensitivity and counterfactual analyses to explore which text features most affect LLM predictions. Overall, we find that current models rely heavily on the relevance of a website to the query, while largely ignoring stylistic features that humans find important.
  arXiv  Detail & Related papers  (2024-02-19T02:15:34Z)
• Can Large Language Models Detect Rumors on Social Media? [21.678652268122296]
  We investigate to use Large Language Models (LLMs) for rumor detection on social media. We propose an LLM-empowered Rumor Detection (LeRuD) approach, in which we design prompts to teach LLMs to reason over important clues in news and comments. LeRuD outperforms several state-of-the-art rumor detection models by 3.2% to 7.7%.
  arXiv  Detail & Related papers  (2024-02-06T11:33:57Z)
• Disinformation Capabilities of Large Language Models [0.564232659769944]
  This paper presents a study of the disinformation capabilities of the current generation of large language models (LLMs) We evaluated the capabilities of 10 LLMs using 20 disinformation narratives. We conclude that LLMs are able to generate convincing news articles that agree with dangerous disinformation narratives.
  arXiv  Detail & Related papers  (2023-11-15T10:25:30Z)
• The ART of LLM Refinement: Ask, Refine, and Trust [85.75059530612882]
  We propose a reasoning with refinement objective called ART: Ask, Refine, and Trust. It asks necessary questions to decide when an LLM should refine its output. It achieves a performance gain of +5 points over self-refinement baselines.
  arXiv  Detail & Related papers  (2023-11-14T07:26:32Z)
• Disinformation Detection: An Evolving Challenge in the Age of LLMs [16.46484369516341]
  Large Language Models (LLMs) can generate highly persuasive yet misleading content. LLMs can be exploited to serve as a robust defense against advanced disinformation. A holistic exploration for the formation and detection of disinformation is conducted to foster this line of research.
  arXiv  Detail & Related papers  (2023-09-25T22:12:50Z)
• 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.