@inproceedings{ceballos-arroyo-etal-2024-open,
title = "Open (Clinical) {LLM}s are Sensitive to Instruction Phrasings",
author = "Ceballos-Arroyo, Alberto Mario and
Munnangi, Monica and
Sun, Jiuding and
Zhang, Karen and
McInerney, Jered and
Wallace, Byron C. and
Amir, Silvio",
editor = "Demner-Fushman, Dina and
Ananiadou, Sophia and
Miwa, Makoto and
Roberts, Kirk and
Tsujii, Junichi",
booktitle = "Proceedings of the 23rd Workshop on Biomedical Natural Language Processing",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.bionlp-1.5",
pages = "50--71",
abstract = "Instruction-tuned Large Language Models (LLMs) can perform a wide range of tasks given natural language instructions to do so, but they are sensitive to how such instructions are phrased. This issue is especially concerning in healthcare, as clinicians are unlikely to be experienced prompt engineers and the potential consequences of inaccurate outputs are heightened in this domain. This raises a practical question: How robust are instruction-tuned LLMs to natural variations in the instructions provided for clinical NLP tasks? We collect prompts from medical doctors across a range of tasks and quantify the sensitivity of seven LLMs{---}some general, others specialized{---}to natural (i.e., non-adversarial) instruction phrasings. We find that performance varies substantially across all models, and that{---}perhaps surprisingly{---}domain-specific models explicitly trained on clinical data are especially brittle, compared to their general domain counterparts. Further, arbitrary phrasing differences can affect fairness, e.g., valid but distinct instructions for mortality prediction yield a range both in overall performance, and in terms of differences between demographic groups.",
}
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<abstract>Instruction-tuned Large Language Models (LLMs) can perform a wide range of tasks given natural language instructions to do so, but they are sensitive to how such instructions are phrased. This issue is especially concerning in healthcare, as clinicians are unlikely to be experienced prompt engineers and the potential consequences of inaccurate outputs are heightened in this domain. This raises a practical question: How robust are instruction-tuned LLMs to natural variations in the instructions provided for clinical NLP tasks? We collect prompts from medical doctors across a range of tasks and quantify the sensitivity of seven LLMs—some general, others specialized—to natural (i.e., non-adversarial) instruction phrasings. We find that performance varies substantially across all models, and that—perhaps surprisingly—domain-specific models explicitly trained on clinical data are especially brittle, compared to their general domain counterparts. Further, arbitrary phrasing differences can affect fairness, e.g., valid but distinct instructions for mortality prediction yield a range both in overall performance, and in terms of differences between demographic groups.</abstract>
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%0 Conference Proceedings
%T Open (Clinical) LLMs are Sensitive to Instruction Phrasings
%A Ceballos-Arroyo, Alberto Mario
%A Munnangi, Monica
%A Sun, Jiuding
%A Zhang, Karen
%A McInerney, Jered
%A Wallace, Byron C.
%A Amir, Silvio
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F ceballos-arroyo-etal-2024-open
%X Instruction-tuned Large Language Models (LLMs) can perform a wide range of tasks given natural language instructions to do so, but they are sensitive to how such instructions are phrased. This issue is especially concerning in healthcare, as clinicians are unlikely to be experienced prompt engineers and the potential consequences of inaccurate outputs are heightened in this domain. This raises a practical question: How robust are instruction-tuned LLMs to natural variations in the instructions provided for clinical NLP tasks? We collect prompts from medical doctors across a range of tasks and quantify the sensitivity of seven LLMs—some general, others specialized—to natural (i.e., non-adversarial) instruction phrasings. We find that performance varies substantially across all models, and that—perhaps surprisingly—domain-specific models explicitly trained on clinical data are especially brittle, compared to their general domain counterparts. Further, arbitrary phrasing differences can affect fairness, e.g., valid but distinct instructions for mortality prediction yield a range both in overall performance, and in terms of differences between demographic groups.
%U https://aclanthology.org/2024.bionlp-1.5
%P 50-71
Markdown (Informal)
[Open (Clinical) LLMs are Sensitive to Instruction Phrasings](https://aclanthology.org/2024.bionlp-1.5) (Ceballos-Arroyo et al., BioNLP-WS 2024)
ACL
- Alberto Mario Ceballos-Arroyo, Monica Munnangi, Jiuding Sun, Karen Zhang, Jered McInerney, Byron C. Wallace, and Silvio Amir. 2024. Open (Clinical) LLMs are Sensitive to Instruction Phrasings. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 50–71, Bangkok, Thailand. Association for Computational Linguistics.