用大模型自动分析志愿者反馈，帮食物救援组织快速定位问题源头。
RescueLens: LLM-Powered Triage and Action on Volunteer Feedback for Food Rescue
- 用大模型自动分类志愿者反馈，识别问题并推荐跟进对象。
- 能捕获96%的问题，精准率达71%，0.5%的捐赠方贡献超30%问题。
- 已部署使用，显著减轻组织工作负担，提升响应效率。
食物救援组织通过志愿者将过剩食品从捐赠方转至有需要的接收方，缓解食物浪费与饥饿问题。志愿者反馈有助于早期发现隐患并提升满意度，但当前依赖人工监控，效率低下且难以优先处理关键问题。本文以匹兹堡412 Food Rescue为合作方，设计了RescueLens——一个基于大语言模型（LLMs）的工具，可自动分类志愿者反馈、建议需跟进的捐赠方与接收方，并根据反馈动态调整志愿者指引。在标注数据集上评估显示，RescueLens在71%精确率下可恢复96%的志愿者问题。通过按问题频率排序捐赠方与接收方，系统使组织仅需关注0.5%的高问题率对象，即可覆盖超过30%的反馈问题。该工具已在412 Food Rescue上线，半结构化访谈表明其显著优化了反馈处理流程，帮助组织更高效分配人力。
原文摘要 · Abstract (English)
Food rescue organizations simultaneously tackle food insecurity and waste by working with volunteers to redistribute food from donors who have excess to recipients who need it. Volunteer feedback allows food rescue organizations to identify issues early and ensure volunteer satisfaction. However, food rescue organizations monitor feedback manually, which can be cumbersome and labor-intensive, making it difficult to prioritize which issues are most important. In this work, we investigate how large language models (LLMs) assist food rescue organizers in understanding and taking action based on volunteer experiences. We work with 412 Food Rescue, a large food rescue organization based in Pittsburgh, Pennsylvania, to design RescueLens, an LLM-powered tool that automatically categorizes volunteer feedback, suggests donors and recipients to follow up with, and updates volunteer directions based on feedback. We evaluate the performance of RescueLens on an annotated dataset, and show that it can recover 96% of volunteer issues at 71% precision. Moreover, by ranking donors and recipients according to their rates of volunteer issues, RescueLens allows organizers to focus on 0.5% of donors responsible for more than 30% of volunteer issues. RescueLens is now deployed at 412 Food Rescue and through semi-structured interviews with organizers, we find that RescueLens streamlines the feedback process so organizers better allocate their time.
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