@inproceedings{su-etal-2026-tamu,
title = "{TAMU}-{NLP} at {A}rch{EHR}-{QA} 2026: Grounded Clinical {QA} with Evidence Identification and Intent-Aware Answer Generation",
author = "Su, Xinqi and
Wang, Rongrong and
Fu, Sunyang and
Liu, Hongfang and
Huang, Ruihong",
editor = "Gupta, Deepak and
Thompson, Paul and
Ananiadou, Sophia and
Demner-Fushman, Dina",
booktitle = "Proceedings of the Third Workshop on Patient-Oriented Language Processing ({CL}4{H}ealth) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.cl4health-1.52/",
doi = "10.63317/55c9e7av4wry",
pages = "551--564",
abstract = "Electronic Health Records (EHRs) contain rich clinical information and provide an important data source for medical question answering. However, generating reliable answers grounded in patient-specific clinical evidence remains challenging. In this work, we participate in the ArchEHR-QA 2026 shared task and focus on Subtask 2 (Evidence Identification) and Subtask 3 (Answer Generation). For evidence identification, we explore both traditional learning-to-rank methods and large language models (LLMs), and propose a two-stage LLM framework that improves prediction stability through few-shot prompting and self-reflection reasoning. For answer generation, we design an intent-aware few-shot prompting framework to generate concise answers grounded in clinical evidence. Experimental results show that our approach achieves strong performance despite limited training data. On the official leaderboard, our system ranks 5th in Subtask 2 and 2nd in Subtask 3. These results demonstrate that combining evidence-driven reasoning with the generative capabilities of LLMs is an effective approach for EHR-based clinical question answering."
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<abstract>Electronic Health Records (EHRs) contain rich clinical information and provide an important data source for medical question answering. However, generating reliable answers grounded in patient-specific clinical evidence remains challenging. In this work, we participate in the ArchEHR-QA 2026 shared task and focus on Subtask 2 (Evidence Identification) and Subtask 3 (Answer Generation). For evidence identification, we explore both traditional learning-to-rank methods and large language models (LLMs), and propose a two-stage LLM framework that improves prediction stability through few-shot prompting and self-reflection reasoning. For answer generation, we design an intent-aware few-shot prompting framework to generate concise answers grounded in clinical evidence. Experimental results show that our approach achieves strong performance despite limited training data. On the official leaderboard, our system ranks 5th in Subtask 2 and 2nd in Subtask 3. These results demonstrate that combining evidence-driven reasoning with the generative capabilities of LLMs is an effective approach for EHR-based clinical question answering.</abstract>
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%0 Conference Proceedings
%T TAMU-NLP at ArchEHR-QA 2026: Grounded Clinical QA with Evidence Identification and Intent-Aware Answer Generation
%A Su, Xinqi
%A Wang, Rongrong
%A Fu, Sunyang
%A Liu, Hongfang
%A Huang, Ruihong
%Y Gupta, Deepak
%Y Thompson, Paul
%Y Ananiadou, Sophia
%Y Demner-Fushman, Dina
%S Proceedings of the Third Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F su-etal-2026-tamu
%X Electronic Health Records (EHRs) contain rich clinical information and provide an important data source for medical question answering. However, generating reliable answers grounded in patient-specific clinical evidence remains challenging. In this work, we participate in the ArchEHR-QA 2026 shared task and focus on Subtask 2 (Evidence Identification) and Subtask 3 (Answer Generation). For evidence identification, we explore both traditional learning-to-rank methods and large language models (LLMs), and propose a two-stage LLM framework that improves prediction stability through few-shot prompting and self-reflection reasoning. For answer generation, we design an intent-aware few-shot prompting framework to generate concise answers grounded in clinical evidence. Experimental results show that our approach achieves strong performance despite limited training data. On the official leaderboard, our system ranks 5th in Subtask 2 and 2nd in Subtask 3. These results demonstrate that combining evidence-driven reasoning with the generative capabilities of LLMs is an effective approach for EHR-based clinical question answering.
%R 10.63317/55c9e7av4wry
%U https://aclanthology.org/2026.cl4health-1.52/
%U https://doi.org/10.63317/55c9e7av4wry
%P 551-564
Markdown (Informal)
[TAMU-NLP at ArchEHR-QA 2026: Grounded Clinical QA with Evidence Identification and Intent-Aware Answer Generation](https://aclanthology.org/2026.cl4health-1.52/) (Su et al., CL4Health 2026)
ACL