{
 "id": "huuuyeah/DeFine",
 "owner": "huuuyeah",
 "name": "DeFine",
 "downloads": 36,
 "likes": 1,
 "last_modified": "2025-07-26T20:49:33.000Z",
 "stated_licence": "cc-by-3.0",
 "stated_from": "tag",
 "licence_name": null,
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 "tasks": [
  "feature-extraction",
  "summarization",
  "question-answering",
  "zero-shot-classification"
 ],
 "citation": "@inproceedings{hu-etal-2025-define,\n    title = \"{D}e{F}ine: Decision-Making with Analogical Reasoning over Factor Profiles\",\n    author = \"Hu, Yebowen  and\n      Wang, Xiaoyang  and\n      Yao, Wenlin  and\n      Lu, Yiming  and\n      Zhang, Daoan  and\n      Foroosh, Hassan  and\n      Yu, Dong  and\n      Liu, Fei\",\n    editor = \"Che, Wanxiang  and\n      Nabende, Joyce  and\n      Shutova, Ekaterina  and\n      Pilehvar, Mohammad Taher\",\n    booktitle = \"Findings of the Association for Computational Linguistics: ACL 2025\",\n    month = jul,\n    year = \"2025\",\n    address = \"Vienna, Austria\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.findings-acl.238/\",\n    pages = \"4587--4603\",\n    ISBN = \"979-8-89176-256-5\",\n    abstract = \"LLMs are ideal for decision-making thanks to their ability to reason over long contexts. However, challenges arise when processing speech transcripts that describe complex scenarios, as they are verbose and include repetition, hedging, and vagueness. E.g., during a company{'}s earnings call, an executive might project a positive revenue outlook to reassure investors, despite uncertainty regarding future earnings. It is crucial for LLMs to incorporate this uncertainty systematically when making decisions. In this paper, we introduce DeFine, a modular framework that constructs probabilistic factor profiles from complex scenarios. It then integrates these profiles with analogical reasoning, leveraging in",
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 "revision": "85b83d50b344bcc40f2ae1fd6d8d3f4a663c4dc4",
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}