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.

通过对比论证综合检索增强的事实验证 (RAFTS) 方法能有效检索相关文档作为证据，并从不同角度评估论证，结合信息丰富的上下文示例作为先验，能在没有复杂提示的情况下显著改善受监督和 LLB 基线，且在相对较小的 LLB 上表现超过基于 GPT 的方法。