Paper

MedicalBench: Evaluating Large Language Models Toward Improved Medical Concept Extraction

Unreadunread

AI summary

arXiv:2605. 20197v1 Announce Type: new Abstract: Medical concept extraction from electronic health records underpins many downstream applications, yet remains challenging because medically meaningful concepts are frequently implied rather than explicitly stated in medical narratives.

Reading status

Priority

About this paper

Authors: Zhichao Yang, Gregory D. Lyng, Sanjit Singh Batra, Robert E. Tillman
Released: 2026-05-21 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because MedicalBench: Evaluating Large Language Models Toward Improved Medical Concept Extraction overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, text, rect, under, correct, eval, line, rate. Source: arxiv cs.CL (NLP).
arXiv: 2605.20197

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to MedicalBench: Evaluating Large Language Models Toward Improved Medical Concept Extraction. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.