Strengthened Symbol Binding Makes Large Language Models Reliable Multiple-Choice Selectors
This work addresses a specific problem of selection bias in LLMs for multiple-choice question answering, which is incremental as it builds on prior research on bias in few-shot scenarios.
The paper tackles selection bias in Large Language Models during Supervised Fine-Tuning for Multiple-Choice Questions by addressing inadequate Multiple Choice Symbol Binding, where models struggle to associate answer options with symbols like A/B/C/D. It introduces the Point-wise Intelligent Feedback (PIF) algorithm, which significantly reduces selection bias and substantially enhances MCQ accuracy.
Multiple-Choice Questions (MCQs) constitute a critical area of research in the study of Large Language Models (LLMs). Previous works have investigated the selection bias problem in MCQs within few-shot scenarios, in which the LLM's performance may be influenced by the presentation of answer choices, leaving the selection bias during Supervised Fine-Tuning (SFT) unexplored. In this paper, we reveal that selection bias persists in the SFT phase , primarily due to the LLM's inadequate Multiple Choice Symbol Binding (MCSB) ability. This limitation implies that the model struggles to associate the answer options with their corresponding symbols (e.g., A/B/C/D) effectively. To enhance the model's MCSB capability, we first incorporate option contents into the loss function and subsequently adjust the weights of the option symbols and contents, guiding the model to understand the option content of the current symbol. Based on this, we introduce an efficient SFT algorithm for MCQs, termed Point-wise Intelligent Feedback (PIF). PIF constructs negative instances by randomly combining the incorrect option contents with all candidate symbols, and proposes a point-wise loss to provide feedback on these negative samples into LLMs. Our experimental results demonstrate that PIF significantly reduces the model's selection bias by improving its MCSB capability. Remarkably, PIF exhibits a substantial enhancement in the accuracy for MCQs.