Users often struggle with decision-making between two options (A vs B), as it usually requires time-consuming research across multiple web pages. We propose STRUM-LLM that addresses this challenge by generating attributed, structured, and helpful contrastive summaries that highlight key differences between the two options. STRUM-LLM identifies helpful contrast: the specific attributes along which the two options differ significantly and which are most likely to influence the user's decision. Our technique is domain-agnostic, and does not require any human-labeled data or fixed attribute list as supervision. STRUM-LLM attributes all extractions back to the input sources along with textual evidence, and it does not have a limit on the length of input sources that it can process. STRUM-LLM Distilled has 100x more throughput than the models with comparable performance while being 10x smaller. In this paper, we provide extensive evaluations for our method and lay out future directions for our currently deployed system.

翻译：用户常因需要在多个网页间进行耗时调研而难以在两种选项（A与B）之间做出决策。本文提出STRUM-LLM方法，通过生成具有归因性、结构化且富有帮助的对比摘要来凸显两选项间的关键差异，从而解决这一挑战。STRUM-LLM能够识别有价值的对比点，即两选项存在显著差异且最可能影响用户决策的特定属性。我们的技术具有领域无关性，无需任何人工标注数据或固定属性列表作为监督信号。STRUM-LLM将所有提取结果归因至输入源并附带文本证据，且对可处理的输入源长度无限制。与性能相当的模型相比，STRUM-LLM蒸馏版本吞吐量提升百倍，体积减小十倍。本文对我们的方法进行了广泛评估，并展望了当前已部署系统的未来发展方向。