Automatic Interactive Evaluation for Large Language Models with State Aware Patient Simulator
This addresses the gap in medical LLM testing for healthcare applications, though it is incremental as it builds on existing evaluation methods by adding interactivity.
The paper tackles the problem of evaluating large language models (LLMs) in realistic clinical scenarios by introducing an Automated Interactive Evaluation (AIE) framework and State-Aware Patient Simulator (SAPS), which uses multi-turn doctor-patient simulations to assess LLMs, with experimental results showing alignment with human evaluations.
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.