The proliferation of online misinformation has posed significant threats to public interest. While numerous online users actively participate in the combat against misinformation, many of such responses can be characterized by the lack of politeness and supporting facts. As a solution, text generation approaches are proposed to automatically produce counter-misinformation responses. Nevertheless, existing methods are often trained end-to-end without leveraging external knowledge, resulting in subpar text quality and excessively repetitive responses. In this paper, we propose retrieval augmented response generation for online misinformation (RARG), which collects supporting evidence from scientific sources and generates counter-misinformation responses based on the evidences. In particular, our RARG consists of two stages: (1) evidence collection, where we design a retrieval pipeline to retrieve and rerank evidence documents using a database comprising over 1M academic articles; (2) response generation, in which we align large language models (LLMs) to generate evidence-based responses via reinforcement learning from human feedback (RLHF). We propose a reward function to maximize the utilization of the retrieved evidence while maintaining the quality of the generated text, which yields polite and factual responses that clearly refutes misinformation. To demonstrate the effectiveness of our method, we study the case of COVID-19 and perform extensive experiments with both in- and cross-domain datasets, where RARG consistently outperforms baselines by generating high-quality counter-misinformation responses.

翻译：网络虚假信息的泛滥已对公共利益构成重大威胁。尽管众多网络用户积极参与打击虚假信息，但此类回应往往缺乏礼貌性和事实依据。作为解决方案，文本生成方法被提出以自动生成反虚假信息回复。然而，现有方法通常采用端到端训练，未能利用外部知识，导致文本质量欠佳且回复过度重复。本文提出面向网络虚假信息的检索增强回复生成方法（RARG），它从科学来源收集支撑证据，并基于这些证据生成反虚假信息回复。具体而言，RARG包含两个阶段：（1）证据收集阶段，我们设计了一个检索管道，利用包含超过100万篇学术文章的数据库检索并重排证据文档；（2）回复生成阶段，我们通过基于人类反馈的强化学习（RLHF）对大语言模型（LLMs）进行对齐，使其生成基于证据的回复。我们提出一种奖励函数，在最大化利用检索证据的同时保持生成文本质量，从而生成清晰反驳虚假信息的礼貌且基于事实的回复。为验证方法有效性，我们以COVID-19案例为研究对象，在领域内和跨领域数据集上进行了大量实验，结果表明RARG通过生成高质量反虚假信息回复，始终优于基线方法。