大型语言模型事实核查的隐患与前景
The Perils & Promises of Fact-checking with Large Language Models
AI总结：
本研究评估了大型语言模型（LLMs）在事实核查任务中的能力与局限，发现配备上下文信息可增强其表现，但准确率受查询语言和声明真实性影响，提示需谨慎使用并进一步研究。
摘要：
自动事实核查利用机器学习验证声明，随着错误信息的传播超出人类事实核查能力，其重要性日益凸显。GPT-4等大型语言模型（LLMs）越来越被信任用于撰写学术论文、法律诉讼和新闻文章，以及验证信息，这强调了它们在辨别真伪中的作用，以及验证其输出结果的重要性。因此，理解LLMs在事实核查任务中的能力与局限，对于确保我们信息生态系统的健康至关重要。在此，我们通过让LLM智能体构建查询、检索上下文数据并做出决策，来评估其在事实核查中的使用。重要的是，在我们的框架中，智能体会解释其推理过程，并从检索到的上下文中引用相关来源。我们的结果表明，当LLMs配备上下文信息时，其能力得到增强。GPT-4优于GPT-3，但准确率因查询语言和声明真实性而异。尽管LLMs在事实核查中显示出潜力，但由于准确率不稳定，谨慎是必要的。我们的研究呼吁进一步探索，以更深入地理解智能体何时成功、何时失败。
英文摘要：
Automated fact-checking, using machine learning to verify claims, has grown vital as misinformation spreads beyond human fact-checking capacity. Large Language Models (LLMs) like GPT-4 are increasingly trusted to write academic papers, lawsuits, and news articles and to verify information, emphasizing their role in discerning truth from falsehood and the importance of being able to verify their outputs. Understanding the capacities and limitations of LLMs in fact-checking tasks is therefore essential for ensuring the health of our information ecosystem. Here, we evaluate the use of LLM agents in fact-checking by having them phrase queries, retrieve contextual data, and make decisions. Importantly, in our framework, agents explain their reasoning and cite the relevant sources from the retrieved context. Our results show the enhanced prowess of LLMs when equipped with contextual information. GPT-4 outperforms GPT-3, but accuracy varies based on query language and claim veracity. While LLMs show promise in fact-checking, caution is essential due to inconsistent accuracy. Our investigation calls for further research, fostering a deeper comprehension of when agents succeed and when they fail.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
