Automatic Evaluation of Healthcare LLMs Beyond Question-Answering
This work addresses the need for better evaluation methods in healthcare LLMs, where both factuality and discourse are critical, though it is incremental in improving existing benchmarking approaches.
The paper tackled the problem of evaluating healthcare LLMs by introducing a multi-axis evaluation suite, revealing blind spots and overlaps in current methods, and proposing a new metric called Relaxed Perplexity to address limitations in open-ended assessments.
Current Large Language Models (LLMs) benchmarks are often based on open-ended or close-ended QA evaluations, avoiding the requirement of human labor. Close-ended measurements evaluate the factuality of responses but lack expressiveness. Open-ended capture the model's capacity to produce discourse responses but are harder to assess for correctness. These two approaches are commonly used, either independently or together, though their relationship remains poorly understood. This work is focused on the healthcare domain, where both factuality and discourse matter greatly. It introduces a comprehensive, multi-axis suite for healthcare LLM evaluation, exploring correlations between open and close benchmarks and metrics. Findings include blind spots and overlaps in current methodologies. As an updated sanity check, we release a new medical benchmark --CareQA-- with both open and closed variants. Finally, we propose a novel metric for open-ended evaluations -- Relaxed Perplexity -- to mitigate the identified limitations.