CoTAR：多粒度的思维链归因推理
CoTAR: Chain-of-Thought Attribution Reasoning with Multi-level Granularity
AI总结：
针对大型语言模型在问答任务中存在的幻觉问题，提出多粒度的思维链归因推理方法CoTAR，通过生成以归因为中心的输出提升归因准确性，经GPT-4在两个数据集验证有效，结合微调还可提升小型模型性能。
摘要：
目前，问答（QA）任务中的最先进性能由采用大型语言模型（LLMs）的系统实现，但这些模型在响应中往往会生成幻觉信息。一种方法侧重于通过整合从给定输入到输出的归因来增强生成过程。然而，识别合适的归因并根据来源验证其准确性是一项复杂任务，需要在评估此类系统方面进行重大改进。我们引入了一种面向归因的思维链推理方法，以提高归因的准确性。该方法将推理过程集中于生成以归因为中心的输出。使用GPT-4在两个上下文增强的问答数据集上进行的评估表明，归因的准确性和正确性得到了提高。此外，我们的方法与微调相结合，提高了两个较小LLMs的响应和归因准确性，显示出它们在某些情况下优于GPT-4的潜力。
英文摘要：
State-of-the-art performance in QA tasks is currently achieved by systems employing Large Language Models (LLMs), however these models tend to hallucinate information in their responses. One approach focuses on enhancing the generation process by incorporating attribution from the given input to the output. However, the challenge of identifying appropriate attributions and verifying their accuracy against a source is a complex task that requires significant improvements in assessing such systems. We introduce an attribution-oriented Chain-of-Thought reasoning method to enhance the accuracy of attributions. This approach focuses the reasoning process on generating an attribution-centric output. Evaluations on two context-enhanced question-answering datasets using GPT-4 demonstrate improved accuracy and correctness of attributions. In addition, the combination of our method with finetuning enhances the response and attribution accuracy of two smaller LLMs, showing their potential to outperform GPT-4 in some cases.
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。
