Automatic Interactive Evaluation for Large Language Models with State Aware Patient Simulator

TL;DR

This paper presents the AIE framework and SAPS simulator to dynamically evaluate large language models in realistic clinical scenarios, improving assessment accuracy for medical applications.

Contribution

Introduction of the AIE framework and SAPS simulator for dynamic, realistic evaluation of medical LLMs in clinical-like multi-turn interactions.

Findings

AIE aligns well with human evaluations.

SAPS provides realistic doctor-patient simulations.

Enhanced assessment of LLMs in healthcare contexts.

Abstract

Large Language Models (LLMs) have demonstrated remarkable proficiency in human interactions, yet their application within the medical field remains insufficiently explored. Previous works mainly focus on the performance of medical knowledge with examinations, which is far from the realistic scenarios, falling short in assessing the abilities of LLMs on clinical tasks. In the quest to enhance the application of Large Language Models (LLMs) in healthcare, this paper introduces the Automated Interactive Evaluation (AIE) framework and the State-Aware Patient Simulator (SAPS), targeting the gap between traditional LLM evaluations and the nuanced demands of clinical practice. Unlike prior methods that rely on static medical knowledge assessments, AIE and SAPS provide a dynamic, realistic platform for assessing LLMs through multi-turn doctor-patient simulations. This approach offers a closer approximation to real clinical scenarios and allows for a detailed analysis of LLM behaviors in response to complex patient interactions. Our extensive experimental validation demonstrates the effectiveness of the AIE framework, with outcomes that align well with human evaluations, underscoring its potential to revolutionize medical LLM testing for improved healthcare delivery.