良好的开端是成功的一半：用于知识增强对话的生成器无关的知识预选择
Well Begun is Half Done: Generator-agnostic Knowledge Pre-Selection for Knowledge-Grounded Dialogue
AI总结：
本文关注知识增强对话中生成前的知识选择，提出生成器无关的方法GATE，通过预选相关知识减轻后续模型负担，实验证明其能有效促进ChatGPT等LLMs生成更具信息量的响应。
摘要：
准确的知识选择在知识增强对话系统中至关重要。为了更深入地研究它，我们提供了一种组织现有文献的新视角，即知识选择与生成耦合、生成后和生成前。我们关注第三类研究不足的类别，它不仅能提前准确选择知识，还能减轻后续响应生成模型（尤其是LLMs）的学习、调整和解释负担。我们提出GATE，一种生成器无关的知识选择方法，通过在不同知识结构和可变知识需求中选择上下文相关知识，为后续响应生成模型准备知识。实验结果证明了GATE的优越性，并表明生成前的知识选择是一种轻量但有效的方式，以促进LLMs（例如ChatGPT）生成更具信息量的响应。
英文摘要：
Accurate knowledge selection is critical in knowledge-grounded dialogue systems. Towards a closer look at it, we offer a novel perspective to organize existing literature, i.e., knowledge selection coupled with, after, and before generation. We focus on the third under-explored category of study, which can not only select knowledge accurately in advance, but has the advantage to reduce the learning, adjustment, and interpretation burden of subsequent response generation models, especially LLMs. We propose GATE, a generator-agnostic knowledge selection method, to prepare knowledge for subsequent response generation models by selecting context-related knowledge among different knowledge structures and variable knowledge requirements. Experimental results demonstrate the superiority of GATE, and indicate that knowledge selection before generation is a lightweight yet effective way to facilitate LLMs (e.g., ChatGPT) to generate more informative responses.
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。
