Mitigating Selection Bias with Node Pruning and Auxiliary Options

• URL: http://arxiv.org/abs/2409.18857v2
• Date: Sat, 17 May 2025 04:21:30 GMT
• Title: Mitigating Selection Bias with Node Pruning and Auxiliary Options
• Authors: Hyeong Kyu Choi, Weijie Xu, Chi Xue, Stephanie Eckman, Chandan K. Reddy,
• Abstract summary: Large language models (LLMs) often exhibit systematic preferences for certain answer choices when responding to multiple-choice questions.<n>This bias reduces the accuracy and reliability of LLM outputs, limiting their usefulness in decision-critical applications.<n>We introduce two methods: Bias Node Pruning (BNP), which prunes parameters that contribute to selection bias, and Auxiliary Option Injection (AOI), which introduces an answer choice to reduce bias in both white-box and black-box settings.
• Score: 11.835002896308545
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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