14 citations · 16 across the 18 of their papers we have counts for
18 papers
Breaking the Likelihood Trap: Variance-Calibrated Modulation for Large Language Model Decoding
Yuanhao Ding, Meimingwei Li, Esteban Garces Arias +3
In open-ended generation, LLMs frequently fall into the "likelihood trap", characterized by repetitive degeneration and vocabulary dullness, resulting in a discrepancy between mach…
Beyond Temperature: Hyperfitting as a Late-Stage Geometric Expansion
Meimingwei Li, Yuanhao Ding, Esteban Garces Arias +1
Recent work has identified a counterintuitive phenomenon termed "Hyperfitting", where fine-tuning Large Language Models (LLMs) to near-zero training loss on small datasets surprisi…
Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration
Chongsheng Zhang, Hao Wang, Zelong Yu +7
Imbalanced data are commonly present in real-world applications. While data synthesis can effectively mitigate data scarcity for rare classes, and LLMs have revolutionized text gen…
Min- Sampling: Decoupling Truncation from Temperature Scaling via Relative Logit Dynamics
Yuanhao Ding, Meimingwei Li, Esteban Garces Arias +3
The quality of text generated by large language models depends critically on the decoding sampling strategy. While mainstream methods such as Top-, Top-, and Min- achieve…
The Truncation Blind Spot: How Decoding Strategies Systematically Exclude Human-Like Token Choices
Esteban Garces Arias, Nurzhan Sapargali, Christian Heumann +1
Why does machine-generated text remain detectable? We investigate a mechanistic explanation at the decoding stage: standard strategies such as top- and nucleus sampling restrict…
The Geometry of Creative Variability: How Credal Sets Expose Calibration Gaps in Language Models
Esteban Garces Arias, Julian Rodemann, Christian Heumann
Understanding uncertainty in large language models remains a fundamental challenge, particularly in creative tasks where multiple valid outputs exist. We present a geometric framew…