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Main Authors: Hou, Yufang, Tran, Thy Thy, Vu, Doan Nam Long, Cao, Yiwen, Li, Kai, Rohde, Lukas, Gurevych, Iryna
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2408.00122
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author Hou, Yufang
Tran, Thy Thy
Vu, Doan Nam Long
Cao, Yiwen
Li, Kai
Rohde, Lukas
Gurevych, Iryna
author_facet Hou, Yufang
Tran, Thy Thy
Vu, Doan Nam Long
Cao, Yiwen
Li, Kai
Rohde, Lukas
Gurevych, Iryna
contents This paper presents a shared task that we organized at the Foundations of Language Technology (FoLT) course in 2023/2024 at the Technical University of Darmstadt, which focuses on evaluating the output of Large Language Models (LLMs) in generating harmful answers to health-related clinical questions. We describe the task design considerations and report the feedback we received from the students. We expect the task and the findings reported in this paper to be relevant for instructors teaching natural language processing (NLP) and designing course assignments.
format Preprint
id arxiv_https___arxiv_org_abs_2408_00122
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A Course Shared Task on Evaluating LLM Output for Clinical Questions
Hou, Yufang
Tran, Thy Thy
Vu, Doan Nam Long
Cao, Yiwen
Li, Kai
Rohde, Lukas
Gurevych, Iryna
Computation and Language
This paper presents a shared task that we organized at the Foundations of Language Technology (FoLT) course in 2023/2024 at the Technical University of Darmstadt, which focuses on evaluating the output of Large Language Models (LLMs) in generating harmful answers to health-related clinical questions. We describe the task design considerations and report the feedback we received from the students. We expect the task and the findings reported in this paper to be relevant for instructors teaching natural language processing (NLP) and designing course assignments.
title A Course Shared Task on Evaluating LLM Output for Clinical Questions
topic Computation and Language
url https://arxiv.org/abs/2408.00122