arXiv Computation and Language August 31, 2026 By Mohammad Arvan, Hossein Haeri, Natalie Parde, Rebecca T. Feinstein

UIC-AIHealth4All at ArchEHR-QA 2026: Answer-First Evidence Grounding for Clinical Question Answering

Read the original on arXiv Computation and Language →

The UIC-AIHealth4All system was presented for the ArchEHR-QA 2026 shared task on grounded question answering from electronic health records. It participated in evidence identification, answer generation, and answer‑evidence alignment, using an answer‑first pipeline that generates candidate answers with cited note sentences before classifying the full evidence set. The system ranked third in evidence identification, ninth in answer generation, and fifth in answer‑evidence alignment, and a linguistic analysis showed its outputs were harder to read than clinician‑authored references, highlighting the need for readability optimization in clinical NLP.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

arXiv AI

Jun 16

EHRNote-ChatQA: A Benchmark for Evidence-Grounded Multi-Turn Clinical Question Answering over Longitudinal Discharge Summaries

arXiv:2606. 15735v1 Announce Type: cross Abstract: Discharge summaries are crucial clinical documents containing the context of a patient's overall hospital stay, and are routinely reviewed by medical experts for patient readmission, ongoing care, and diagnostic decision-making.

By Jiyoun Kim, Muhan Yeo, Eunhye Jang, Jeewon Yang, Hangyul Yoon, Su Ji Lee, Hee Jo Han, Hee-Jae Jung, Doyun Kwon, Jun young Lee, Jaehun Lee, Jung-Oh Lee, Sunjun Kweon, Jong Hak Moon, Daseul Kim, Minjae Cho, Edward Choi

llms nlp benchmarks

arXiv AI

Jul 8

From Voting to Agent Collaboration: Answer-Type-Aware LLM Pipelines for BioASQ 14b

arXiv:2607. 06452v1 Announce Type: cross Abstract: Biomedical question answering requires not only accurate extraction of information from scientific literature but also reliable integration of evidence across multiple documents.

By Taeyun Roh, Eunha Lee, Wonjune Jang, Sohyun Chung, Junha Jung, Jaewoo Kang

llms agents nlp

arXiv Computation and Language

Aug 24

MedRAGChecker: Claim-Level Verification for Biomedical Retrieval-Augmented Generation

MedRAGChecker is a claim-level verification framework designed for biomedical retrieval‑augmented generation (RAG). It decomposes generated answers into atomic claims and assesses each claim’s support by combining evidence‑grounded natural language inference with biomedical knowledge‑graph consistency signals. The aggregated claim decisions provide diagnostics that distinguish retrieval and generation failures, such as faithfulness, under‑evidence, contradiction, and safety‑critical errors, and the system is distilled into compact models for scalable evaluation.

By Yuelyu Ji, Min Gu Kwak, Hang Zhang, Xizhi Wu, Chenyu Li, Yanshan Wang

llms rag benchmarks safety

arXiv AI

Jul 20

Ruling Out to Rule In: Contrastive Hypothesis Retrieval for Medical Question Answering

arXiv:2604. 04593v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) grounds large language models in external medical knowledge, yet standard retrievers frequently surface hard negatives that are semantically close to the query but describe clinically distinct conditions.

By Byeolhee Kim, Min-Kyung Kim, Young-Hak Kim, Tae-Joon Jeon

llms rag nlp benchmarks

arXiv AI

Jul 9

Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answering

arXiv:2607. 06641v1 Announce Type: cross Abstract: Large language models (LLMs) achieve promising results on medical question answering benchmarks, yet their use in public health is constrained by hallucinations and the rapid evolution of official guidance.

By Felix Feldman, Joshua Harris, Timothy Laurence, Leo Loman, Ollie Higgins, Fan Grayson, Poonam Soma, Bethany Pace-Bonello, Michael Borowitz, Toby Nonnenmacher

llms rag nlp benchmarks safety

arXiv AI

Aug 18

Toward Better Assessment of LLMs' Performance in Clinical Error Detection

arXiv:2608. 16643v1 Announce Type: cross Abstract: Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation.

By Yifan Zhang, Rahmatollah Beheshti

llms nlp benchmarks safety

One email a morning, machine-written

Related stories

EHRNote-ChatQA: A Benchmark for Evidence-Grounded Multi-Turn Clinical Question Answering over Longitudinal Discharge Summaries

From Voting to Agent Collaboration: Answer-Type-Aware LLM Pipelines for BioASQ 14b

MedRAGChecker: Claim-Level Verification for Biomedical Retrieval-Augmented Generation

Ruling Out to Rule In: Contrastive Hypothesis Retrieval for Medical Question Answering

Healthier LLMs: Retrieval-Augmented Generation for Public Health Question Answering

Toward Better Assessment of LLMs' Performance in Clinical Error Detection