Ranking Over Scoring: Towards Reliable and Robust Automated Evaluation of LLM-Generated Medical Explanatory Arguments
<|reference_start|>Ranking Over Scoring: Towards Reliable and Robust Automated Evaluation of LLM-Generated Medical Explanatory Arguments: 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.<|reference_end|>
@article{de la iglesia2024ranking,
    title={Ranking Over Scoring: Towards Reliable and Robust Automated Evaluation
  of LLM-Generated Medical Explanatory Arguments},
    author={Iker De la Iglesia, Iakes Goenaga, Johanna Ramirez-Romero, Jose Maria
  Villa-Gonzalez, Josu Goikoetxea and Ander Barrena},
    journal={arXiv preprint arXiv:2409.20565},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.20565},
    primaryClass={cs.CL}
}