4 citations · 6 across the 10 of their papers we have counts for
5 papers · 1 filter
Stay Focused: Problem Drift in Multi-Agent Debate
Jonas Becker, Lars Benedikt Kaesberg, Andreas Stephan +3
Multi-agent debate - multiple instances of large language models discussing problems in turn-based interaction - has shown promise for solving knowledge and reasoning tasks. Howeve…
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…
Analysing zero-shot temporal relation extraction on clinical notes using temporal consistency
Vasiliki Kougia, Anastasiia Sedova, Andreas Stephan +2
This paper presents the first study for temporal relation extraction in a zero-shot setting focusing on biomedical text. We employ two types of prompts and five LLMs (GPT-3.5, Mixt…
Weaker Than You Think: A Critical Look at Weakly Supervised Learning
Dawei Zhu, Xiaoyu Shen, Marius Mosbach +2
Weakly supervised learning is a popular approach for training machine learning models in low-resource settings. Instead of requesting high-quality yet costly human annotations, it…
WeaNF: Weak Supervision with Normalizing Flows
Andreas Stephan, Benjamin Roth
A popular approach to decrease the need for costly manual annotation of large data sets is weak supervision, which introduces problems of noisy labels, coverage and bias. Methods f…