RECALL：大语言模型对抗外部反事实知识鲁棒性的基准
RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge
AI总结：
该研究构建RECALL基准，通过问答和文本生成任务评估大语言模型对抗外部反事实知识的鲁棒性，发现现有模型易受干扰且简单干预效果有限。
摘要：
大语言模型和人工智能聊天机器人提高了人们在各个领域的效率。然而，回答问题所需的知识可能超出模型的知识边界。为了缓解这一问题，许多研究者尝试将外部知识（如知识图谱和互联网内容）引入大语言模型，以获取最新信息。然而，来自互联网的外部信息可能包含反事实信息，这会混淆模型并导致错误回答。因此，大语言模型迫切需要具备从外部知识中区分可靠信息的能力。为了评估大语言模型辨别外部知识可靠性的能力，我们从现有知识库中创建了一个基准。我们的基准包含两个任务：问答和文本生成，对于每个任务，我们向模型提供包含反事实信息的上下文。评估结果表明，现有大语言模型容易受到带有反事实信息的不可靠外部知识的干扰，而简单的干预方法对缓解这一问题贡献有限。
英文摘要：
LLMs and AI chatbots have improved people's efficiency in various fields. However, the necessary knowledge for answering the question may be beyond the models' knowledge boundaries. To mitigate this issue, many researchers try to introduce external knowledge, such as knowledge graphs and Internet contents, into LLMs for up-to-date information. However, the external information from the Internet may include counterfactual information that will confuse the model and lead to an incorrect response. Thus there is a pressing need for LLMs to possess the ability to distinguish reliable information from external knowledge. Therefore, to evaluate the ability of LLMs to discern the reliability of external knowledge, we create a benchmark from existing knowledge bases. Our benchmark consists of two tasks, Question Answering and Text Generation, and for each task, we provide models with a context containing counterfactual information. Evaluation results show that existing LLMs are susceptible to interference from unreliable external knowledge with counterfactual information, and simple intervention methods make limited contributions to the alleviation of this issue.
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。
