LLM-as-a-Fuzzy-Judge: Fine-Tuning Large Language Models as a Clinical Evaluation Judge with Fuzzy Logic (2506.11221v1)

Abstract: Clinical communication skills are critical in medical education, and practicing and assessing clinical communication skills on a scale is challenging. Although LLM-powered clinical scenario simulations have shown promise in enhancing medical students' clinical practice, providing automated and scalable clinical evaluation that follows nuanced physician judgment is difficult. This paper combines fuzzy logic and LLM and proposes LLM-as-a-Fuzzy-Judge to address the challenge of aligning the automated evaluation of medical students' clinical skills with subjective physicians' preferences. LLM-as-a-Fuzzy-Judge is an approach that LLM is fine-tuned to evaluate medical students' utterances within student-AI patient conversation scripts based on human annotations from four fuzzy sets, including Professionalism, Medical Relevance, Ethical Behavior, and Contextual Distraction. The methodology of this paper started from data collection from the LLM-powered medical education system, data annotation based on multidimensional fuzzy sets, followed by prompt engineering and the supervised fine-tuning (SFT) of the pre-trained LLMs using these human annotations. The results show that the LLM-as-a-Fuzzy-Judge achieves over 80\% accuracy, with major criteria items over 90\%, effectively leveraging fuzzy logic and LLM as a solution to deliver interpretable, human-aligned assessment. This work suggests the viability of leveraging fuzzy logic and LLM to align with human preferences, advances automated evaluation in medical education, and supports more robust assessment and judgment practices. The GitHub repository of this work is available at https://github.com/2sigmaEdTech/LLMAsAJudge

• The paper proposes "LLM-as-a-Fuzzy-Judge," a novel model integrating Large Language Models with fuzzy logic to evaluate medical students' clinical communication skills, addressing scalability and consistency issues.
• The hybrid model, fine-tuned on human annotations using fuzzy sets (Professionalism, Medical Relevance, etc.), achieved over 80% accuracy and strong alignment with human judges' nuanced evaluations.
• This approach offers a scalable and interpretable solution for automated clinical assessment, with potential applications in other complex educational or decision-making fields.

Evaluating Clinical Communication Skills Using LLMs and Fuzzy Logic

The paper "LLM-as-a-Fuzzy-Judge: Fine-Tuning LLMs as a Clinical Evaluation Judge with Fuzzy Logic," by Weibing Zheng and collaborators, presents a novel approach to enhance the evaluation of medical students' clinical communication skills. The methodology involves the integration of LLMs with fuzzy logic principles to approximate the nuanced judgment typical of experienced clinical evaluators, addressing the challenge of scalability and consistency in assessments.

Methodological Approach

The paper details a systematic method starting with data collection from an LLM-powered medical education system, 2-Sigma. The system generates conversation scripts between medical students and AI-simulated patients, providing a robust dataset for analysis. The researchers then employed human annotation based on four fuzzy sets: Professionalism, Medical Relevance, Ethical Behavior, and Contextual Distraction, which capture the subtleties often involved in clinical assessments.

Fuzzy logic is particularly suitable for modeling the gradations and uncertainties inherent in human judgment, seamlessly aligning automated evaluations with the expert opinions typical in clinical settings. The paper exploits this by representing each evaluation criterion as a fuzzy set and adopting an LLM that is fine-tuned via supervised learning using these human annotations. The innovative aspect of combining LLMs and fuzzy logic allows the model not only to learn from the annotations but also to engineer prompts that reflect human-style contextual evaluations. This results in a multi-task learning environment where intricate evaluation patterns are effectively captured.

Key Results and Evaluation

The LLM-as-a-Fuzzy-Judge achieved an accuracy of over 80% across all fuzzy criteria, with certain crucial judgment levels attaining over 90%. These results indicate that the hybrid model, leveraging both supervised fine-tuning (SFT) and prompt engineering, offers superior performance in comparison to simpler baseline models.

Remarkably, the hybrid model delivers strong alignment with human judges' evaluations by producing nuanced, interpretable judgments consistent with human feedback. Such alignment demonstrates the potential of the methodology in capturing subtle distinctions in clinical interactions—an aspect traditionally challenging for automated systems.

Implications and Future Directions

The paper’s implications for medical education are substantial, providing a scalable solution for the evaluation of clinical skills while maintaining the interpretability and alignment that are critical in healthcare scenarios. This approach not only supports educational institutions in offering consistent assessments but aids in minimizing the resources required for manual evaluations.

In terms of theoretical advancements, it illustrates how fuzzy logic can complement AI systems in domains requiring context-dependent judgments. Future work might explore extending this model to other complex educational and decision-making fields, such as legal studies or ethics-oriented sectors. Enhancements in fuzzy set criteria or incorporating additional human feedback through active learning or reinforcement learning mechanisms could further refine its applicability and reliability.

In conclusion, the paper showcases a promising integration of LLMs and fuzzy logic in clinical education, paving the way towards more robust automated assessment solutions that are both scalable and closely aligned with expert judgment.