title: Language Models are Surprisingly Fragile to Drug Names in Biomedical
    Benchmarks
summary: 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.
paper_authors._id: 66722b0886f6b3d126722a0e
paper_authors.name: Jack Gallifant
paper_authors.status: admin_assigned
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paper_authors.name: Shan Chen
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paper_authors.fullname: Shan Chen
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paper_authors.user: shanchen
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paper_authors.name: Pedro Moreira
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paper_authors.fullname: Pedro Moreira
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paper_authors.user: pedromoreira
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paper_authors.name: Nikolaj Munch
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paper_authors.fullname: Nikolaj Munch
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paper_authors.user: NikolajMunch
paper_authors._id: 66722b0886f6b3d126722a12
paper_authors.name: Mingye Gao
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paper_authors.fullname: Mingye Gao
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paper_authors.user: mingye94
paper_authors._id: 66722b0886f6b3d126722a13
paper_authors.name: Jackson Pond
paper_authors.status: admin_assigned
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paper_authors._id: 657a8b6d8b9601d838fa0734
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paper_authors.fullname: Jackson Pond
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paper_authors.user: jackdpond
paper_authors._id: 66722b0886f6b3d126722a14
paper_authors.name: Leo Anthony Celi
paper_authors._id: 66722b0886f6b3d126722a15
paper_authors.name: Hugo Aerts
paper_authors._id: 66722b0886f6b3d126722a16
paper_authors.name: Thomas Hartvigsen
paper_authors._id: 66722b0886f6b3d126722a17
paper_authors.name: Danielle Bitterman
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-20T10:21:36.108Z
paper_authors._id: 6570cd33f3853f99bc305e55
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paper_authors.fullname: Danielle Bitterman
paper_authors.type: user
paper_authors.user: daniellebitt
paper_title: Language Models are Surprisingly Fragile to Drug Names in Biomedical
    Benchmarks
paper_summary: 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.
paper_ai_keywords: LLMs (Large Language Models) MedQA MedMCQA pre-training datasets