The CALLA Dataset: Probing LLMs' Interactive Knowledge Acquisition from Chinese Medical Literature

The application of Large Language Models (LLMs) to the medical domain has stimulated the interest of researchers. Recent studies have focused on constructing Instruction Fine-Tuning (IFT) data through medical knowledge graphs to enrich the interactive medical knowledge of LLMs. However, the medical literature serving as a rich source of medical knowledge remains unexplored. Our work introduces the CALLA dataset to probe LLMs' interactive knowledge acquisition from Chinese medical literature. It assesses the proficiency of LLMs in mastering medical knowledge through a free-dialogue fact-checking task. We identify a phenomenon called the ``fact-following response``, where LLMs tend to affirm facts mentioned in questions and display a reluctance to challenge them. To eliminate the inaccurate evaluation caused by this phenomenon, for the golden fact, we artificially construct test data from two perspectives: one consistent with the fact and one inconsistent with the fact. Drawing from the probing experiment on the CALLA dataset, we conclude that IFT data highly correlated with the medical literature corpus serves as a potent catalyst for LLMs, enabling themselves to skillfully employ the medical knowledge acquired during the pre-training phase within interactive scenarios, enhancing accuracy. Furthermore, we design a framework for automatically constructing IFT data based on medical literature and discuss some real-world applications.