FactLLaMA：利用外部知识优化指令跟随语言模型以实现自动事实核查
FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking
AI总结：
针对大语言模型知识滞后问题，本文提出FactLLaMA，结合外部证据检索与指令微调LLaMA，在RAWFC和LIAR数据集上实现最先进的事实核查性能。
摘要：
自动事实核查在打击错误信息传播中扮演着至关重要的角色。大型语言模型（LLMs）及其指令跟随变体，如InstructGPT和Alpaca，在各种自然语言处理任务中展现出了卓越的性能。然而，它们的知识可能并非总是最新或充分的，这可能导致事实核查中的不准确性。为了解决这一局限性，我们提出将指令跟随语言模型的能力与外部证据检索相结合，以增强事实核查性能。我们的方法涉及利用搜索引擎为给定的输入声明检索相关证据。这些外部证据作为有价值的补充信息，用以增强预训练语言模型的知识。然后，我们使用这些证据对开源的LLaMA语言模型进行指令微调，使其能够更准确地预测输入声明的真实性。为了评估我们的方法，我们在两个广泛使用的事实核查数据集RAWFC和LIAR上进行了实验。结果表明，我们的方法在事实核查任务中达到了最先进的性能。通过整合外部证据，我们弥合了模型知识与最新且充分可用上下文之间的差距，从而改善了事实核查结果。我们的发现对于打击错误信息以及促进在线平台上准确信息的传播具有重要意义。我们发布的相关材料可在以下网址获取：https://thcheung.github.io/factllama。
英文摘要：
Automatic fact-checking plays a crucial role in combating the spread of misinformation. Large Language Models (LLMs) and Instruction-Following variants, such as InstructGPT and Alpaca, have shown remarkable performance in various natural language processing tasks. However, their knowledge may not always be up-to-date or sufficient, potentially leading to inaccuracies in fact-checking. To address this limitation, we propose combining the power of instruction-following language models with external evidence retrieval to enhance fact-checking performance. Our approach involves leveraging search engines to retrieve relevant evidence for a given input claim. This external evidence serves as valuable supplementary information to augment the knowledge of the pretrained language model. Then, we instruct-tune an open-sourced language model, called LLaMA, using this evidence, enabling it to predict the veracity of the input claim more accurately. To evaluate our method, we conducted experiments on two widely used fact-checking datasets: RAWFC and LIAR. The results demonstrate that our approach achieves state-of-the-art performance in fact-checking tasks. By integrating external evidence, we bridge the gap between the model's knowledge and the most up-to-date and sufficient context available, leading to improved fact-checking outcomes. Our findings have implications for combating misinformation and promoting the dissemination of accurate information on online platforms. Our released materials are accessible at: https://thcheung.github.io/factllama.
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。
