标题： LLM摘要中的文档估值：集群Shapley方法 显示英文标题

标题： Document Valuation in LLM Summaries: A Cluster Shapley Approach

摘要： 大型语言模型（LLMs）越来越多地被用于检索和汇总来自多个来源的内容的系统中，例如搜索引擎和人工智能助手。 尽管这些模型通过生成连贯的摘要提升了用户体验，但它们模糊了原始内容创作者的贡献，引发了关于功劳归属和补偿的问题。 我们解决了评估LLM生成的摘要中使用的单个文档价值的挑战。 我们提议使用Shapley值，这是一种基于每篇文档边际贡献来分配功劳的游戏论方法。 虽然理论上很有吸引力，但在大规模计算Shapley值成本高昂。 因此，我们提出了Cluster Shapley，一种利用文档之间语义相似性的高效近似算法。 通过使用基于LLM的嵌入聚类文档并在集群级别计算Shapley值，我们的方法显著减少了计算量，同时保持了功劳归属的质量。 我们在亚马逊产品评论的摘要任务中展示了我们的方法。 Cluster Shapley显著降低了计算复杂度，同时保持了高精度，在效率前沿方面优于基线方法，例如蒙特卡洛抽样和Kernel SHAP。 我们的方法与所用的具体LLM、所用的摘要过程以及评估程序无关，这使其广泛适用于各种摘要设置。 显示英文摘要

摘要： Large Language Models (LLMs) are increasingly used in systems that retrieve and summarize content from multiple sources, such as search engines and AI assistants. While these models enhance user experience by generating coherent summaries, they obscure the contributions of original content creators, raising concerns about credit attribution and compensation. We address the challenge of valuing individual documents used in LLM-generated summaries. We propose using Shapley values, a game-theoretic method that allocates credit based on each document's marginal contribution. Although theoretically appealing, Shapley values are expensive to compute at scale. We therefore propose Cluster Shapley, an efficient approximation algorithm that leverages semantic similarity between documents. By clustering documents using LLM-based embeddings and computing Shapley values at the cluster level, our method significantly reduces computation while maintaining attribution quality. We demonstrate our approach to a summarization task using Amazon product reviews. Cluster Shapley significantly reduces computational complexity while maintaining high accuracy, outperforming baseline methods such as Monte Carlo sampling and Kernel SHAP with a better efficient frontier. Our approach is agnostic to the exact LLM used, the summarization process used, and the evaluation procedure, which makes it broadly applicable to a variety of summarization settings.