{"ID":2845181,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.04070","arxiv_id":"2511.04070","title":"T-FIX: Text-Based Explanations with Features Interpretable to eXperts","abstract":"As LLMs are deployed in knowledge-intensive settings (e.g., surgery, astronomy, therapy), users are often domain experts who expect not just answers, but explanations that mirror professional reasoning. Yet evaluating whether an LLM \"thinks like an expert\" remains difficult: existing approaches rely on per-example expert annotation, making them costly, hard to scale, and tied to a single notion of correct reasoning within each domain. To address this gap, we introduce T-FIX, a unified evaluation framework that operationalizes expert alignment as a desired attribute of LLM-generated explanations. T-FIX spans seven scientific tasks across three domains, with each task evaluated against expert-defined criteria that capture domain-grounded reasoning rather than generic explanation quality. Our framework enables automatic, personalizable evaluation of expert alignment that generalizes to unseen explanations without ongoing expert involvement. Code is available at https://github.com/BrachioLab/FIX-2/.","short_abstract":"As LLMs are deployed in knowledge-intensive settings (e.g., surgery, astronomy, therapy), users are often domain experts who expect not just answers, but explanations that mirror professional reasoning. Yet evaluating whether an LLM \"thinks like an expert\" remains difficult: existing approaches rely on per-example expe...","url_abs":"https://arxiv.org/abs/2511.04070","url_pdf":"https://arxiv.org/pdf/2511.04070v3","authors":"[\"Shreya Havaldar\",\"Weiqiu You\",\"Chaehyeon Kim\",\"Anton Xue\",\"Helen Jin\",\"Marco Gatti\",\"Bhuvnesh Jain\",\"Helen Qu\",\"Amin Madani\",\"Daniel A. Hashimoto\",\"Gary E. Weissman\",\"Rajat Deo\",\"Sameed Khatana\",\"Lyle Ungar\",\"Eric Wong\"]","published":"2025-11-06T05:19:54Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Large Language Model\"]","has_code":false,"code_links":[{"ID":607349,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2845181,"paper_url":"https://arxiv.org/abs/2511.04070","paper_title":"T-FIX: Text-Based Explanations with Features Interpretable to eXperts","repo_url":"https://github.com/BrachioLab/FIX-2","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}