1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
promptolution: A Unified, Modular Framework for Prompt Optimization
Tom Zehle, Timo Heiß, Moritz Schlager +2
Prompt optimization has become crucial for enhancing the performance of large language models (LLMs) across a broad range of tasks. Although many research papers demonstrate its ef…
Reinforcement Learning for Latent-Space Thinking in LLMs
Enes Özeren, Matthias Aßenmacher
Chain-of-Thought (CoT) reasoning typically utilizes the discrete language space for thinking, which is inherently inefficient, as many generated tokens only enforce linguistic rule…
AI Conversational Interviewing: Transforming Surveys with LLMs as Adaptive Interviewers
Alexander Wuttke, Matthias Aßenmacher, Christopher Klamm +3
Traditional methods for eliciting people's opinions face a trade-off between depth and scale: structured surveys enable large-scale data collection but limit respondents' ability t…
From Calculation to Adjudication: Examining LLM judges on Mathematical Reasoning Tasks
Andreas Stephan, Dawei Zhu, Matthias Aßenmacher +2
To reduce the need for human annotations, large language models (LLMs) have been proposed as judges of the quality of other candidate models. The performance of LLM judges is typic…