Go to ICLR 2025 Conference homepageLarge Language Models Can Be More Robust Multiple Choice Selectors Through Attention Intervention
Multiple-choice question (MCQ) is a common task for evaluating large language models (LLMs). LLMs' performance on MCQ is often affected by various biases. Previous research has extensively examined the impact of inherent option bias on MCQ predictions, where this bias refers to a preference for a specific option ID token introduced during the model's training. However, in an in-context learning scenario, few-shot prompting can also introduce a form of bias, known as context option bias. This occurs, for instance, in extreme cases where all demonstration answers are consistently option A, in which case LLMs may predict A for the given question whatever the question is. Context option bias can significantly degrade LLMs' performance. To better observe the LLMs' behavior when affected by the context option bias, we deliberately use demonstrations with obvious context option bias for MCQ to amplify the effect. The results indicate that certain attention heads in LLMs are particularly sensitive to context option bias. Motivated by this observation, we propose our approach, CoLo, to address this issue. First, using samples with ordinary and biased demonstrations as input, CoLo compares the outputs of two types of inputs and localizes attention heads sensitive to context option bias through sequential interventions. Then, we propose an attention scaling-based method to intervene in the identified attention heads during the inference stage, thereby mitigating the impact of context option bias on the LLMs’ predictions. Experimental results demonstrate that CoLo effectively alleviates the impact of context option bias and improves the LLM's robustness on MCQ tasks.