Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments

• URL: http://arxiv.org/abs/2406.09815v1
• Date: Fri, 14 Jun 2024 08:13:34 GMT
• Title: Retrieval Augmented Fact Verification by Synthesizing Contrastive Arguments
• Authors: Zhenrui Yue, Huimin Zeng, Lanyu Shang, Yifan Liu, Yang Zhang, Dong Wang,
• Abstract summary: We propose retrieval augmented fact verification through the synthesis of contrasting arguments. Our method effectively retrieves relevant documents as evidence and evaluates arguments from varying perspectives. We demonstrate the effectiveness of our method through extensive experiments, where RAFTS can outperform GPT-based methods with a significantly smaller 7B LLM.
• Score: 23.639378586798884
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid propagation of misinformation poses substantial risks to public interest. To combat misinformation, large language models (LLMs) are adapted to automatically verify claim credibility. Nevertheless, existing methods heavily rely on the embedded knowledge within LLMs and / or black-box APIs for evidence collection, leading to subpar performance with smaller LLMs or upon unreliable context. In this paper, we propose retrieval augmented fact verification through the synthesis of contrasting arguments (RAFTS). Upon input claims, RAFTS starts with evidence retrieval, where we design a retrieval pipeline to collect and re-rank relevant documents from verifiable sources. Then, RAFTS forms contrastive arguments (i.e., supporting or refuting) conditioned on the retrieved evidence. In addition, RAFTS leverages an embedding model to identify informative demonstrations, followed by in-context prompting to generate the prediction and explanation. Our method effectively retrieves relevant documents as evidence and evaluates arguments from varying perspectives, incorporating nuanced information for fine-grained decision-making. Combined with informative in-context examples as prior, RAFTS achieves significant improvements to supervised and LLM baselines without complex prompts. We demonstrate the effectiveness of our method through extensive experiments, where RAFTS can outperform GPT-based methods with a significantly smaller 7B LLM.

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