Inference Functions in Large Language Models: A Comprehensive Framework for Bias Mitigation

Abstract

Large Language Models (LLMs) have become a cornerstone of natural language processing tasks across industries. However, these models often perpetuate the biases present in the training data, resulting in harmful societal impacts. Bias in LLMs can manifest in various forms, such as gender stereotypes, racial prejudice, and cultural misrepresentation. This paper introduces Inference Functions, a post-processing mechanism designed to dynamically detect and mitigate bias in real-time during the inference stage. Unlike traditional bias mitigation techniques, which require pre-processing data or retraining models, inference functions offer a scalable and efficient solution by intervening after the model generates outputs. We explore the design, application, and trade-offs of inference functions for bias mitigation, backed by experiments and case studies. We also discuss the ethical implications and potential for compliance with emerging AI regulations.