利用显式工作记忆提升事实准确性
Improving Factuality with Explicit Working Memory
AI总结：
本文提出显式工作记忆方法EWE，通过实时事实核查与检索反馈动态更新记忆，在长文本生成中纠正虚假陈述，在四个数据集上将事实性指标VeriScore绝对提升2至6个百分点且不损害有用性。
摘要：
大型语言模型可能生成事实不准确的内容，这一问题被称为幻觉。近期研究基于检索增强生成，通过迭代提示来提升事实准确性，但这些方法受限于传统的RAG设计。为应对这些挑战，我们提出EWE（Explicit Working Memory，显式工作记忆），这是一种新方法，通过集成一个从外部资源接收实时反馈的工作记忆，来增强长文本生成中的事实准确性。该记忆根据在线事实核查和检索反馈进行刷新，使EWE能够在生成过程中纠正虚假陈述，从而确保输出更准确、更可靠。我们的实验表明，EWE在四个以事实查询为导向的长文本生成数据集上优于强基线，在事实性指标VeriScore上绝对提升了2到6个百分点，同时没有牺牲回答的有用性。进一步分析揭示，记忆更新的规则设计、记忆单元的配置以及检索数据存储的质量是影响模型性能的关键因素。
英文摘要：
Large language models can generate factually inaccurate content, a problem known as hallucination. Recent works have built upon retrieved-augmented generation to improve factuality through iterative prompting but these methods are limited by the traditional RAG design. To address these challenges, we introduce EWE (Explicit Working Memory), a novel approach that enhances factuality in long-form text generation by integrating a working memory that receives real-time feedback from external resources. The memory is refreshed based on online fact-checking and retrieval feedback, allowing EWE to rectify false claims during the generation process and ensure more accurate and reliable outputs. Our experiments demonstrate that Ewe outperforms strong baselines on four fact-seeking long-form generation datasets, increasing the factuality metric, VeriScore, by 2 to 6 points absolute without sacrificing the helpfulness of the responses. Further analysis reveals that the design of rules for memory updates, configurations of memory units, and the quality of the retrieval datastore are crucial factors for influencing model performance.
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。
