4 citations · 6 across the 10 of their papers we have counts for
10 papers
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…
Text-Guided Alternative Image Clustering
Andreas Stephan, Lukas Miklautz, Collin Leiber +4
Traditional image clustering techniques only find a single grouping within visual data. In particular, they do not provide a possibility to explicitly define multiple types of clus…
Counterfactual Reasoning with Knowledge Graph Embeddings
Lena Zellinger, Andreas Stephan, Benjamin Roth
Knowledge graph embeddings (KGEs) were originally developed to infer true but missing facts in incomplete knowledge repositories. In this paper, we link knowledge graph completion…
Text-Guided Image Clustering
Andreas Stephan, Lukas Miklautz, Kevin Sidak +4
Image clustering divides a collection of images into meaningful groups, typically interpreted post-hoc via human-given annotations. Those are usually in the form of text, begging t…
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…
SepLL: Separating Latent Class Labels from Weak Supervision Noise
Andreas Stephan, Vasiliki Kougia, Benjamin Roth
In the weakly supervised learning paradigm, labeling functions automatically assign heuristic, often noisy, labels to data samples. In this work, we provide a method for learning f…
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…
Knodle: Modular Weakly Supervised Learning with PyTorch
Anastasiia Sedova, Andreas Stephan, Marina Speranskaya +1
Strategies for improving the training and prediction quality of weakly supervised machine learning models vary in how much they are tailored to a specific task or integrated with a…