Gabliteration: Adaptive Multi-Directional Neural Weight Modification for Selective Behavioral Alteration in Large Language Models
This addresses a fundamental limitation for researchers and practitioners working with large language models who need to selectively alter model behaviors.
The paper tackles the problem of modifying specific behavioral patterns in large language models without compromising overall model quality, achieving theoretically superior weight modification while minimizing quality degradation in unrelated domains through the Gabliteration technique.
We present Gabliteration, a novel neural weight modification technique that advances beyond traditional abliteration methods by implementing adaptive multi-directional projections with regularized layer selection. Our approach addresses the fundamental limitation of existing methods that compromise model quality while attempting to modify specific behavioral patterns. Through dynamic layer optimization, regularized projection matrices, and adaptive scaling mechanisms, we achieve theoretically superior weight modification while minimizing quality degradation in unrelated domains. We validate our method through the gabliterated-v1 model series (0.6B to 4B parameters) available on Hugging Face, demonstrating practical applicability across multiple model scales.