"Internal Stabilization Framework for Reducing Hallucination in Large Language Models"
Year: 2025
Type: book
Abstract: Hallucination remains one of the most fundamental limitations of modern Large Language Models (LLMs), yet most mitigation strategies focus only on external signals such as retrieval or post-hoc verification. This paper introduces a breakthrough internal stabilization framework that directly targets representational drift — the hidden-state instability that causes hallucination inside the model’s own generative process. We formalize drift as a measurable cross-layer divergence and demonstrate how internal semantic trajectories can be dynamically stabilized without reducing creativity or impairing reasoning depth. Our approach constructs a multi-stage semantic checkpointing mechanism and a consistency-preserving feedback loop that actively steers the model’s internal geometry toward stable, truth-aligned states. Experiments across legal, medical, financial, and open-domain benchmarks show up to 50% hallucination reduction while maintaining generative fluency and high-resolution reasoning. By shifting the discussion from external patching to internal cognitive stabilization, this work opens a new research direction for building trustworthy, self-consistent, and cognitively grounded AI systems.
Author: JUN, DAEDO
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7108642425
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