Chongsheng Zhang

Breaking the Likelihood Trap: Variance-Calibrated Modulation for Large Language Model Decoding

In experiments across open-ended generation, factual QA, and mathematical reasoning, it is shown that VCM consistently mitigates the likelihood trap, and integrates with existing decoding strategies, improving diversity and coherence and, particularly at higher decoding temperatures, reasoning accuracy.

Concept-factorized multi-view clustering with orthogonal diversity and manifold regularization