Embracing Positional Bias in Multiple-Choice Question Answering via Permutation Equivariant Neural Networks

Abstract

Several studies have demonstrated that large language models (LLMs) exhibit positional bias when answering multiple-choice questions (MCQs). Previous methods have identified such bias to be detrimental, leading to the development of techniques to mitigate it. However, we observe that certain permutations of options can actually improve the performance. Therefore, instead of eliminating such bias, we propose an EMbracing the Bias EquivaRiantly (EMBER) network. Specifically, the EMBER network, which outputs a permutation of options in MCQs, is optimized towards the beneficial permutations to which the LLM is biased. Additionally, to solve the positional bias among different permutations of options, the EMBER network is designed to grant the equivariance to the permutation to the LLMs. Theoretically and empirically, we show that the proposed EMBER network can effectively utilize the positional bias and demonstrate state-of-the-art performance over various baselines.