Ranking Over Scoring: Towards Reliable and Robust Automated Evaluation of LLM-Generated Medical Explanatory Arguments
This addresses the challenge of reliable and robust automated evaluation in the medical domain, though it is incremental as it builds on existing evaluation methods with specific improvements.
The paper tackles the problem of evaluating LLM-generated medical explanatory arguments by introducing a novel methodology using Proxy Tasks and rankings to align with human evaluation and reduce biases, demonstrating robustness against adversarial attacks and requiring minimal human-crafted examples (e.g., one per Proxy Task).
Evaluating LLM-generated text has become a key challenge, especially in domain-specific contexts like the medical field. This work introduces a novel evaluation methodology for LLM-generated medical explanatory arguments, relying on Proxy Tasks and rankings to closely align results with human evaluation criteria, overcoming the biases typically seen in LLMs used as judges. We demonstrate that the proposed evaluators are robust against adversarial attacks, including the assessment of non-argumentative text. Additionally, the human-crafted arguments needed to train the evaluators are minimized to just one example per Proxy Task. By examining multiple LLM-generated arguments, we establish a methodology for determining whether a Proxy Task is suitable for evaluating LLM-generated medical explanatory arguments, requiring only five examples and two human experts.