Mitigating Selection Bias with Node Pruning and Auxiliary Options

TL;DR

This paper introduces two novel methods, Bias Node Pruning and Auxiliary Option Injection, to reduce selection bias in large language models, improving accuracy and reliability in multiple-choice tasks.

Contribution

It presents a new bias mitigation approach by removing internal bias sources and introduces a novel evaluation metric, CKLD, for better bias measurement.

Findings

Methods improve answer accuracy across models and datasets.

Bias reduction is effective in both white-box and black-box settings.

Proposed metrics better capture distributional biases.

Abstract

Large language models (LLMs) often exhibit systematic preferences for certain answer choices when responding to multiple-choice questions-a behavior known as selection bias. This bias reduces the accuracy and reliability of LLM outputs, limiting their usefulness in decision-critical applications. While prior work has focused on adjusting model inputs or outputs to mitigate this issue, our work takes a fundamentally different approach by identifying and removing the internal sources of bias. We introduce two methods: Bias Node Pruning (BNP), which prunes parameters that contribute to selection bias, and Auxiliary Option Injection (AOI), which introduces an additional answer choice to reduce bias in both white-box and black-box settings. To address the shortcomings of existing evaluation metrics, we propose Choice Kullback-Leibler Divergence (CKLD), a new metric that captures distributional imbalances in model predictions. Experiments on three LLMs across multiple datasets demonstrate that our methods consistently improve answer accuracy while reducing selection bias, providing a robust solution for both open- and closed-source models.