RescueLens: LLM-Powered Triage and Action on Volunteer Feedback for Food Rescue
This addresses the labor-intensive feedback process for food rescue organizers, streamlining their workflow to better allocate time, though it is an incremental application of existing LLM methods to a new domain.
The paper tackles the problem of manually monitoring volunteer feedback in food rescue organizations by developing RescueLens, an LLM-powered tool that automatically categorizes feedback and suggests follow-ups, achieving 96% recall and 71% precision in recovering volunteer issues and identifying that 0.5% of donors are responsible for over 30% of issues.
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.