Author

Christian Heumann

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#machine learning Mar 2026

The Truncation Blind Spot: How Decoding Strategies Systematically Exclude Human-Like Token Choices

Probability-floor samplers substantially narrow the blind spot, demonstrating that the choice of truncation criterion matters for retaining human-used tokens and motivate decoding methods that preserve contextually appropriate low-probability choices while maintaining generation quality.

E. Arias, Nurzhan Sapargali, Christian Heumann et al. · arXiv.org · 2 citations

#natural language process... Jun 2026

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.

Yuanhao Ding, Meimingwei Li, E. Arias et al. · arXiv.org · 2 citations