Beyond Hidden-Layer Manipulation: Semantically-Aware Logit Interventions for Debiasing LLMs

ABSTRACT We proposed Static and Dynamic--two zero-shot logits-layer debiasing methods. Dynamic reduces bias by up to 70% with minimal fluency loss. Logits intervention outperforms hidden-layer approaches. We show semantic-aware logits intervention is stable and effective for debiasing aligned LLMs. Index T erms-- LLM Alignment,Debiasing 1. INTRODUCTION The rapid advancement of Large Language Models (LLMs) has revolutionized natural language processing, but their growing complexity raises critical concerns about trustworthiness [1].