{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T12:07:05Z","timestamp":1764850025847,"version":"3.46.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686387","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T00:00:00Z","timestamp":1764633600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,2]]},"abstract":"<jats:p>Each year millions of people seek help for their legal problems by calling a legal aid program hotline, walking into a legal aid office, or using a lawyer referral service. The first step to match them to the right help is to identify the legal problem the applicant is experiencing. Misdirected applicants may miss a deadline, experience physical abuse, lose housing or lose custody of children while waiting to connect to the right legal help. We introduce and evaluate the FETCH classifier for legal issue classification and describe two methods for improving accuracy: a hybrid LLM\/ML ensemble classification method, and the automatic generation of follow-up questions to enrich the initial problem narrative. We employ a novel dataset of 419 real-world queries to a nonprofit lawyer referral service. We achieve 97.37% accuracy (hits@2) with inexpensive models, slightly above GPT-5, while reducing cost of guiding users to the right legal resource.<\/jats:p>","DOI":"10.3233\/faia251588","type":"book-chapter","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T12:04:52Z","timestamp":1764849892000},"source":"Crossref","is-referenced-by-count":0,"title":["That\u2019s So FETCH: Fashioning Ensemble Techniques for LLM Classification in Civil Legal Intake and Referral"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0110-064X","authenticated-orcid":false,"given":"Quinten","family":"Steenhuis","sequence":"first","affiliation":[{"name":"Suffolk University Law School, Boston, MA"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Legal Knowledge and Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251588","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T12:04:53Z","timestamp":1764849893000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251588"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,2]]},"ISBN":["9781643686387"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251588","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,2]]}}}