ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examination
This addresses the problem of language bias and insufficient medical resources in interpretability evaluation for LLMs, but it is incremental as it extends existing work to a new domain and language.
The authors tackled the lack of linguistic and thematic diversity in explanation datasets for LLMs by introducing ExplainCPE, a Chinese medical benchmark with over 7k instances, and found that ChatGPT and GPT-4 have limitations in text understanding and computational reasoning.
As ChatGPT and GPT-4 spearhead the development of Large Language Models (LLMs), more researchers are investigating their performance across various tasks. But more research needs to be done on the interpretability capabilities of LLMs, that is, the ability to generate reasons after an answer has been given. Existing explanation datasets are mostly English-language general knowledge questions, which leads to insufficient thematic and linguistic diversity. To address the language bias and lack of medical resources in generating rationales QA datasets, we present ExplainCPE (over 7k instances), a challenging medical benchmark in Simplified Chinese. We analyzed the errors of ChatGPT and GPT-4, pointing out the limitations of current LLMs in understanding text and computational reasoning. During the experiment, we also found that different LLMs have different preferences for in-context learning. ExplainCPE presents a significant challenge, but its potential for further investigation is promising, and it can be used to evaluate the ability of a model to generate explanations. AI safety and trustworthiness need more attention, and this work makes the first step to explore the medical interpretability of LLMs.The dataset is available at https://github.com/HITsz-TMG/ExplainCPE.