通过面向校准的检索增强生成实现可靠决策
Reliable Decision Making via Calibration Oriented Retrieval Augmented Generation
- KAIST（韩国科学技术院）
- Kookmin University（韩国釜山大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对LLM在决策中可能提供错误信息的问题，提出CalibRAG检索方法，通过确保RAG决策校准良好，提升校准性能与准确性。
AI中文摘要：
近年来，大型语言模型（LLMs）越来越多地被用于支持各种决策任务，协助人类做出明智的决策。然而，当LLMs自信地提供错误信息时，可能导致人类做出次优决策。为了防止LLMs在不确定的主题上生成错误信息，并提高生成内容的准确性，先前的工作提出了检索增强生成（RAG），即引用外部文档来生成响应。然而，先前的RAG方法仅关注检索与输入查询最相关的文档，并未专门旨在确保人类用户的决策得到良好校准。为解决这一局限性，我们提出了一种新颖的检索方法，称为校准检索增强生成（CalibRAG），该方法确保由RAG所告知的决策得到良好校准。随后，我们通过实验验证，与各种数据集上的其他基线相比，CalibRAG在提高校准性能以及准确性方面均有所改进。
英文摘要：
Recently, Large Language Models (LLMs) have been increasingly used to support various decision-making tasks, assisting humans in making informed decisions. However, when LLMs confidently provide incorrect information, it can lead humans to make suboptimal decisions. To prevent LLMs from generating incorrect information on topics they are unsure of and to improve the accuracy of generated content, prior works have proposed Retrieval Augmented Generation (RAG), where external documents are referenced to generate responses. However, previous RAG methods focus only on retrieving documents most relevant to the input query, without specifically aiming to ensure that the human user's decisions are well-calibrated. To address this limitation, we propose a novel retrieval method called Calibrated Retrieval-Augmented Generation (CalibRAG), which ensures that decisions informed by RAG are well-calibrated. Then we empirically validate that CalibRAG improves calibration performance as well as accuracy, compared to other baselines across various datasets.
