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A Clinical Trial Design Approach to Auditing Language Models in Healthcare Setting

arXiv 2411.16702 ↗First posted 11 Nov 2024cs.CY cs.LGcited by 0

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Abstract (arXiv)

We present an audit mechanism for language models, with a focus on models deployed in the healthcare setting. Our proposed mechanism takes inspiration from clinical trial design where we posit the language model audit as a single blind equivalence trial, with the comparison of interest being the subject matter experts. We show that using our proposed method, we can follow principled sample size and power calculations, leading to the requirement of sampling minimum number of records while maintaining the audit integrity and statistical soundness. Finally, we provide a real-world example of the audit used in a production environment in a large-scale public health network.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 28 references that are in the map. First posted on arXiv 11 Nov 2024.

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References · 18

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  5. 2020Longformer: The Long-Document Transformercited by 3,362
  6. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
  7. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  8. 2023A Survey on Evaluation of Large Language Modelscited by 1,227
  9. 2018Model Cards for Model Reportingcited by 1,068
  10. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 888
  11. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 695
  12. 2023How Language Model Hallucinations Can Snowballcited by 310
6 more
  1. 2016Model-Agnostic Interpretability of Machine Learningcited by 219
  2. 2023Automatically Auditing Large Language Models via Discrete Optimizationcited by 187
  3. 2023Auditing large language models: a three-layered approachcited by 135
  4. 2019Phenotyping of Clinical Notes with Improved Document Classification Models Using Contextualized Neural Language Modelscited by 20
  5. 2020Comparative Analysis of Text Classification Approaches in Electronic Health Recordscited by 12
  6. 2017Comparing Rule-Based and Deep Learning Models for Patient Phenotypingcited by 3

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