BIAS MITIGATION STRATEGIES FOR LANGUAGE MODELS THROUGH CONTROLLED TEXT GENERATION

Abstract

Large language models (LLMs) have demonstrated remarkable proficiency across a range of natural language processing (NLP) tasks. However, their widespread use has also highlighted issues of societal, gender, racial, and political bias embedded within generated content. This paper explores structured methods for mitigating bias through controlled text generation techniques, categorizing strategies into pre-training adjustments, in-training methods, and post-generation filtering. By analyzing state-of-the-art methods before 2022 and introducing control mechanisms like conditional generation, decoding constraints, and reinforcement learning-based reward shaping, we illustrate the performance trade-offs between model fluency and fairness. Visual models and comparative analysis emphasize how these methods function and interrelate.