@inproceedings{vaish-sharma-2026-ltrc,
title = "{LTRC}-{IIIT} at {MEDIQA}-{SYNUR} 2026: Benchmarking a Fully Local, Training-Free {RAG} Pipeline",
author = "Vaish, Aashwin and
Sharma, Dipti Misra",
editor = "Ben Abacha, Asma and
Bethard, Steven and
Bitterman, Danielle and
Naumann, Tristan and
Roberts, Kirk",
booktitle = "Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical {NLP}) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.clinicalnlp-1.15/",
doi = "10.63317/3wbuccufqusz",
pages = "136--140",
abstract = "In this paper we present our solution to MEDIQA-SYNUR 2026 shared task organized at LREC-ClinicalNLP workshop. The goal of the task is to populate Electronic Health Record (EHR) flowsheets using the transcriptions of nurse dictations, to alleviate the extensive manual labor associated with sifting through large flowsheets of clinical concepts. We propose a modular architecture combining heuristic-driven Retrieval-Augmented Generation (RAG) with grammar-constrained decoding on an open-weight, quantized, 8B-parameter model (Llama 3.1 Instruct). Our system achieves an F1 score of 0.57, significantly trailing the initial zero-shot experiments with GPT-4o and placing it towards the lower end of the current leaderboard. We conduct a failure analysis of this approach while establishing a baseline for privacy-preserving, zero-shot documentation assistants."
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<abstract>In this paper we present our solution to MEDIQA-SYNUR 2026 shared task organized at LREC-ClinicalNLP workshop. The goal of the task is to populate Electronic Health Record (EHR) flowsheets using the transcriptions of nurse dictations, to alleviate the extensive manual labor associated with sifting through large flowsheets of clinical concepts. We propose a modular architecture combining heuristic-driven Retrieval-Augmented Generation (RAG) with grammar-constrained decoding on an open-weight, quantized, 8B-parameter model (Llama 3.1 Instruct). Our system achieves an F1 score of 0.57, significantly trailing the initial zero-shot experiments with GPT-4o and placing it towards the lower end of the current leaderboard. We conduct a failure analysis of this approach while establishing a baseline for privacy-preserving, zero-shot documentation assistants.</abstract>
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%0 Conference Proceedings
%T LTRC-IIIT at MEDIQA-SYNUR 2026: Benchmarking a Fully Local, Training-Free RAG Pipeline
%A Vaish, Aashwin
%A Sharma, Dipti Misra
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Bitterman, Danielle
%Y Naumann, Tristan
%Y Roberts, Kirk
%S Proceedings of the 8th Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F vaish-sharma-2026-ltrc
%X In this paper we present our solution to MEDIQA-SYNUR 2026 shared task organized at LREC-ClinicalNLP workshop. The goal of the task is to populate Electronic Health Record (EHR) flowsheets using the transcriptions of nurse dictations, to alleviate the extensive manual labor associated with sifting through large flowsheets of clinical concepts. We propose a modular architecture combining heuristic-driven Retrieval-Augmented Generation (RAG) with grammar-constrained decoding on an open-weight, quantized, 8B-parameter model (Llama 3.1 Instruct). Our system achieves an F1 score of 0.57, significantly trailing the initial zero-shot experiments with GPT-4o and placing it towards the lower end of the current leaderboard. We conduct a failure analysis of this approach while establishing a baseline for privacy-preserving, zero-shot documentation assistants.
%R 10.63317/3wbuccufqusz
%U https://aclanthology.org/2026.clinicalnlp-1.15/
%U https://doi.org/10.63317/3wbuccufqusz
%P 136-140
Markdown (Informal)
[LTRC-IIIT at MEDIQA-SYNUR 2026: Benchmarking a Fully Local, Training-Free RAG Pipeline](https://aclanthology.org/2026.clinicalnlp-1.15/) (Vaish & Sharma, ClinicalNLP 2026)
ACL