Probing the Dual Logic Ability of Privatized Medical-Domain LLMs

Large Language Models (LLMs) are gaining widespread attention for their potential across various fields, particularly within the medical domain. Recent efforts have aimed to privatize general-domain LLMs into specialized medical-domain LLMs by feeding high-quality, medical-domain training data. However, an overlooked aspect in these privatization efforts is the Dual Logic Ability of LLMs. This ability enables LLMs to comprehend pairs of logically opposed questions, ensuring stance consistency in their responses. In our study, we investigate two primary questions: Q1. How does privatization affect the dual logic ability of LLMs? Q2. How can we maintain the robustness of LLMs' dual logic ability after privatization? To explore these questions, we first constructed a medical-domain dual logic ability evaluation dataset comprising logically opposed question pairs, created manually by NLP experts. By examining the stance consistency in responses to logically opposed question pairs, our analysis demonstrates a significant decline in the dual logic ability of LLMs after privatization. Furthermore, we construct privatization data to investigate the effects of the pre-training and instruction fine-tuning stages on the dual logic ability of LLMs. Interestingly, our findings reveal that the instruction fine-tuning stage often inadvertently compromises the LLMs' dual logic ability although it is not the trainers' intention. To counteract this, we incorporated general-domain dual logic data derived from basic science during the instruction fine-tuning stage, which are automatically constructed by our designed pipeline. Experiment results show that privatized LLMs can generalize dual logic ability from general-domain dual logic data, leading to their enhanced performance in the medical domain. Our study underscores the importance of prioritizing LLMs' dual logic ability during the privatization process and establishes a benchmark for future research.