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Total papers: 3 Search mode: keyword Shortlist (0) RSS

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A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Models

Maria Mahbub, Gregory M. Dams, Josh Arnold, Caitlin Rizy, Sudarshan Srinivasan, Elliot M. Fielstein · Apr 7, 2026

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 100% High protocol signal Freshness: Hot Status: Ready
Expert Verification Automatic Metrics MedicineMultilingual
  • Conventional evaluation methods rely heavily on annotation-intensive reference standards or incomplete structured data, limiting feasibility at population scale.
  • Using judge-evaluated outputs as references, the primary LLM achieved an F1 score of 0.80 under relaxed matching criteria.
Open paper
MENLO: From Preferences to Proficiency -- Evaluating and Modeling Native-like Quality Across 47 Languages

Chenxi Whitehouse, Sebastian Ruder, Tony Lin, Oksana Kurylo, Haruka Takagi, Janice Lam · Sep 30, 2025

Citations: 0

Match reason: Keyword overlap 1/1 across title and protocol fields.

Score: 98% High protocol signal Freshness: Cold Status: Fallback
Pairwise PreferenceRubric Rating Automatic Metrics Multilingual
  • To address this, we introduce MENLO, a framework that operationalizes the evaluation of native-like response quality based on audience design-inspired mechanisms.
  • Additionally, we show that RL-trained judges can serve as generative reward models to enhance LLMs' multilingual proficiency, though discrepancies with human judgment remain.
Open paper
Blinded Radiologist and LLM-Based Evaluation of LLM-Generated Japanese Translations of Chest CT Reports: Comparative Study

Yosuke Yamagishi, Atsushi Takamatsu, Yasunori Hamaguchi, Tomohiro Kikuchi, Shouhei Hanaoka, Takeharu Yoshikawa · Apr 2, 2026

Citations: 0

Match reason: Matched by broad semantic/index fallback.

Score: 65% Moderate protocol signal Freshness: Hot Status: Ready
Pairwise Preference Llm As JudgeAutomatic Metrics MedicineMultilingual
  • A board-certified radiologist and a radiology resident independently performed blinded pairwise evaluations across 4 criteria: terminology accuracy, readability, overall quality, and radiologist-style authenticity.
  • Radiologist 2 rated readability as equivalent in 75% of cases and favored the human-edited translation for overall quality (40% vs 21%).
Open paper

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