Showing cs.CLShow all
3 papers · 1 filter
cs.CL2026
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…
cs.CL2025
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…
cs.CL2024
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…