用大模型自动检索并筛选社交平台上的已验证言论，帮事实核查员提速。
A Generative-AI-Driven Claim Retrieval System Capable of Detecting and Retrieving Claims from Social Media Platforms in Multiple Languages
- 用大语言模型过滤无关已验证声明，提升检索效率。
- 人类评估显示可显著减少重复核查工作量。
- 适合需要快速响应虚假信息的媒体与平台团队。
网络虚假信息带来全球性挑战，事实核查员需高效验证信息以遏制假消息传播。当前主要问题在于重复核查已验证过的声明，增加工作负担并延迟对新出现声明的响应。本文提出一种生成式AI驱动的声明检索系统，能够从社交媒体中检索先前已核实的声明，评估其与输入内容的相关性，并提供补充信息支持核查工作。该方法利用大语言模型（LLMs）过滤无关声明，并生成简洁摘要与解释，帮助核查员快速判断某声明是否已被验证。通过自动化与人工评估相结合的方式验证效果，结果显示，大语言模型能有效排除大量不相关声明，从而降低工作负荷，优化核查流程。
原文摘要 · Abstract (English)
Online disinformation poses a global challenge, placing significant demands on fact-checkers who must verify claims efficiently to prevent the spread of false information. A major issue in this process is the redundant verification of already fact-checked claims, which increases workload and delays responses to newly emerging claims. This research introduces an approach that retrieves previously fact-checked claims, evaluates their relevance to a given input, and provides supplementary information to support fact-checkers. Our method employs large language models (LLMs) to filter irrelevant fact-checks and generate concise summaries and explanations, enabling fact-checkers to faster assess whether a claim has been verified before. In addition, we evaluate our approach through both automatic and human assessments, where humans interact with the developed tool to review its effectiveness. Our results demonstrate that LLMs are able to filter out many irrelevant fact-checks and, therefore, reduce effort and streamline the fact-checking process.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。