4 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", marked by repetitive degeneration and vocabulary dullness, creating a discrepancy between machine-generat…
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…
Revisiting Active Learning under (Human) Label Variation
Cornelia Gruber, Helen Alber, Bernd Bischl +3
Access to high-quality labeled data remains a limiting factor in applied supervised learning. While label variation (LV), i.e., differing labels for the same instance, is common, e…
Algorithmic Fidelity of Large Language Models in Generating Synthetic German Public Opinions: A Case Study
Bolei Ma, Berk Yoztyurk, Anna-Carolina Haensch +5
In recent research, large language models (LLMs) have been increasingly used to investigate public opinions. This study investigates the algorithmic fidelity of LLMs, i.e., the abi…