Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors

• URL: http://arxiv.org/abs/2403.09747v1
• Date: Thu, 14 Mar 2024 00:35:39 GMT
• Title: Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors
• Authors: Guanghua Li, Wensheng Lu, Wei Zhang, Defu Lian, Kezhong Lu, Rui Mao, Kai Shu, Hao Liao,
• Abstract summary: Large Language Models (LLMs) are known for their remarkable reasoning and generative capabilities. We introduce a novel, retrieval-augmented LLMs framework--the first of its kind to automatically and strategically extract key evidence from web sources for claim verification. Our framework ensures the acquisition of sufficient, relevant evidence, thereby enhancing performance.
• Score: 38.75533934195315
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The proliferation of fake news has had far-reaching implications on politics, the economy, and society at large. While Fake news detection methods have been employed to mitigate this issue, they primarily depend on two essential elements: the quality and relevance of the evidence, and the effectiveness of the verdict prediction mechanism. Traditional methods, which often source information from static repositories like Wikipedia, are limited by outdated or incomplete data, particularly for emerging or rare claims. Large Language Models (LLMs), known for their remarkable reasoning and generative capabilities, introduce a new frontier for fake news detection. However, like traditional methods, LLM-based solutions also grapple with the limitations of stale and long-tail knowledge. Additionally, retrieval-enhanced LLMs frequently struggle with issues such as low-quality evidence retrieval and context length constraints. To address these challenges, we introduce a novel, retrieval-augmented LLMs framework--the first of its kind to automatically and strategically extract key evidence from web sources for claim verification. Employing a multi-round retrieval strategy, our framework ensures the acquisition of sufficient, relevant evidence, thereby enhancing performance. Comprehensive experiments across three real-world datasets validate the framework's superiority over existing methods. Importantly, our model not only delivers accurate verdicts but also offers human-readable explanations to improve result interpretability.

Related papers

• Vietnamese Legal Information Retrieval in Question-Answering System [0.0]
  Retrieval Augmented Generation (RAG) has gained significant recognition for enhancing the capabilities of large language models (LLMs) However, RAG often fall short when applied to the Vietnamese language due to several challenges. This report introduces our three main modifications taken to address these challenges.
  arXiv  Detail & Related papers  (2024-09-05T02:34:05Z)
• Multimodal Misinformation Detection using Large Vision-Language Models [7.505532091249881]
  Large language models (LLMs) have shown remarkable performance in various tasks. Few approaches consider evidence retrieval as part of misinformation detection. We propose a novel re-ranking approach for multimodal evidence retrieval.
  arXiv  Detail & Related papers  (2024-07-19T13:57:11Z)
• How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models [95.44559524735308]
  Large language or multimodal model based verification has been proposed to scale up online policing mechanisms for mitigating spread of false and harmful content. We test the limits of improving foundation model performance without continual updating through an initial study of knowledge transfer. Our results on two recent multi-modal fact-checking benchmarks, Mocheg and Fakeddit, indicate that knowledge transfer strategies can improve Fakeddit performance over the state-of-the-art by up to 1.7% and Mocheg performance by up to 2.9%.
  arXiv  Detail & Related papers  (2024-06-29T08:39:07Z)
• Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challenges [21.425647152424585]
  We propose a strong fake news attack method called conditional Variational-autoencoder-Like Prompt (VLPrompt) Unlike current methods, VLPrompt eliminates the need for additional data collection while maintaining contextual coherence. Our experiments, including various detection methods and novel human study metrics, were conducted to assess their performance on our dataset.
  arXiv  Detail & Related papers  (2024-03-27T04:39:18Z)
• 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)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• Multiverse: Multilingual Evidence for Fake News Detection [71.51905606492376]
  Multiverse is a new feature based on multilingual evidence that can be used for fake news detection. The hypothesis of the usage of cross-lingual evidence as a feature for fake news detection is confirmed.
  arXiv  Detail & Related papers  (2022-11-25T18:24:17Z)
• Interpretable Fake News Detection with Topic and Deep Variational Models [2.15242029196761]
  We focus on fake news detection using interpretable features and methods. We have developed a deep probabilistic model that integrates a dense representation of textual news. Our model achieves comparable performance to state-of-the-art competing models.
  arXiv  Detail & Related papers  (2022-09-04T05:31:00Z)
• A Systematic Review on the Detection of Fake News Articles [0.0]
  It has been argued that fake news and the spread of false information pose a threat to societies throughout the world. To combat this threat, a number of Natural Language Processing (NLP) approaches have been developed. This paper aims to delineate the approaches for fake news detection that are most performant, identify limitations with existing approaches, and suggest ways these can be mitigated.
  arXiv  Detail & Related papers  (2021-10-18T21:29:11Z)
• Detecting Cross-Modal Inconsistency to Defend Against Neural Fake News [57.9843300852526]
  We introduce the more realistic and challenging task of defending against machine-generated news that also includes images and captions. To identify the possible weaknesses that adversaries can exploit, we create a NeuralNews dataset composed of 4 different types of generated articles. In addition to the valuable insights gleaned from our user study experiments, we provide a relatively effective approach based on detecting visual-semantic inconsistencies.
  arXiv  Detail & Related papers  (2020-09-16T14:13:15Z)

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.