Q-STRUM Debate：面向推荐比较的查询驱动对比摘要
Q-STRUM Debate: Query-Driven Contrastive Summarization for Recommendation Comparison
AI总结：
本文提出 Q-STRUM Debate，通过辩论式提示扩展 STRUM-LLM，利用大语言模型生成查询相关的聚焦对比摘要，在三个数据集上显著提升了查询驱动对比摘要的性能。
摘要：
针对未知项目的查询驱动推荐给用户理解为何某些项目适合其需求带来了挑战。查询驱动对比摘要（QCS）是一种旨在通过利用基于语言的项目描述来阐明项目之间对比的方法。然而，现有的最先进对比摘要方法（如 STRUM-LLM）未能实现这一目标。为克服这些局限，我们提出了 Q-STRUM Debate，这是 STRUM-LLM 的一种新型扩展，采用辩论式提示来生成与查询相关的项目方面的聚焦对比摘要。借助现代大语言模型（LLMs）作为生成辩论的强大工具，Q-STRUM Debate 提供了增强的对比摘要。在三个数据集上的实验表明，Q-STRUM Debate 在关键对比摘要指标上相比现有方法取得了显著的性能提升，从而为 QCS 引入了一种新颖且高效的辩论提示方法。
英文摘要：
Query-driven recommendation with unknown items poses a challenge for users to understand why certain items are appropriate for their needs. Query-driven Contrastive Summarization (QCS) is a methodology designed to address this issue by leveraging language-based item descriptions to clarify contrasts between them. However, existing state-of-the-art contrastive summarization methods such as STRUM-LLM fall short of this goal. To overcome these limitations, we introduce Q-STRUM Debate, a novel extension of STRUM-LLM that employs debate-style prompting to generate focused and contrastive summarizations of item aspects relevant to a query. Leveraging modern large language models (LLMs) as powerful tools for generating debates, Q-STRUM Debate provides enhanced contrastive summaries. Experiments across three datasets demonstrate that Q-STRUM Debate yields significant performance improvements over existing methods on key contrastive summarization criteria, thus introducing a novel and performant debate prompting methodology for QCS.
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。
