Language Models are Surprisingly Fragile to Drug Names in Biomedical
  Benchmarks
Medical knowledge is context-dependent and requires consistent reasoning
across various natural language expressions of semantically equivalent phrases.
This is particularly crucial for drug names, where patients often use brand
names like Advil or Tylenol instead of their generic equivalents. To study
this, we create a new robustness dataset, RABBITS, to evaluate performance
differences on medical benchmarks after swapping brand and generic drug names
using physician expert annotations.
  We assess both open-source and API-based LLMs on MedQA and MedMCQA, revealing
a consistent performance drop ranging from 1-10\%. Furthermore, we identify a
potential source of this fragility as the contamination of test data in widely
used pre-training datasets. All code is accessible at
https://github.com/BittermanLab/RABBITS, and a HuggingFace leaderboard is
available at https://huggingface.co/spaces/AIM-Harvard/rabbits-leaderboard.
66722b0886f6b3d126722a0e
Jack Gallifant
admin_assigned
2024-06-20T10:20:59.524Z
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/avatars/0ebd56d949b55cf17696a81fea48ff62.svg
jack gallifant
user
gallifantjack
66722b0886f6b3d126722a0f
Shan Chen
claimed_verified
2024-06-19T07:38:57.688Z
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Shan Chen
user
shanchen
66722b0886f6b3d126722a10
Pedro Moreira
admin_assigned
2024-06-20T10:21:08.212Z
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Pedro Moreira
user
pedromoreira
66722b0886f6b3d126722a11
Nikolaj Munch
claimed_verified
2024-06-19T20:20:57.974Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1665233398140-61969bb4db91685d2ef3e5a0.png
Nikolaj Munch
user
NikolajMunch
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Mingye Gao
admin_assigned
2024-06-20T10:21:14.256Z
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Mingye Gao
user
mingye94
66722b0886f6b3d126722a13
Jackson Pond
admin_assigned
2024-06-20T10:21:20.240Z
657a8b6d8b9601d838fa0734
/avatars/94f5cf786f0ee3a1a8255e434be93a9c.svg
Jackson Pond
user
jackdpond
66722b0886f6b3d126722a14
Leo Anthony Celi
66722b0886f6b3d126722a15
Hugo Aerts
66722b0886f6b3d126722a16
Thomas Hartvigsen
66722b0886f6b3d126722a17
Danielle Bitterman
admin_assigned
2024-06-20T10:21:36.108Z
6570cd33f3853f99bc305e55
https://cdn-avatars.huggingface.co/v1/production/uploads/6570cd33f3853f99bc305e55/70ZnaOtdtmy8DeWVJpCKb.png
Danielle Bitterman
user
daniellebitt
Language Models are Surprisingly Fragile to Drug Names in Biomedical
  Benchmarks
Medical knowledge is context-dependent and requires consistent reasoning
across various natural language expressions of semantically equivalent phrases.
This is particularly crucial for drug names, where patients often use brand
names like Advil or Tylenol instead of their generic equivalents. To study
this, we create a new robustness dataset, RABBITS, to evaluate performance
differences on medical benchmarks after swapping brand and generic drug names
using physician expert annotations.
  We assess both open-source and API-based LLMs on MedQA and MedMCQA, revealing
a consistent performance drop ranging from 1-10\%. Furthermore, we identify a
potential source of this fragility as the contamination of test data in widely
used pre-training datasets. All code is accessible at
https://github.com/BittermanLab/RABBITS, and a HuggingFace leaderboard is
available at https://huggingface.co/spaces/AIM-Harvard/rabbits-leaderboard.
A new dataset evaluates the robustness of LLMs in medical benchmarks by swapping brand and generic drug names, revealing performance drops attributed to test data contamination in pre-training datasets.
robustness dataset RABBITS MedQA MedMCQA open-source LLMs API-based LLMs test data contamination