3 citations · 6 across the 8 of their papers we have counts for
11 papers
PolBiX: Detecting LLMs' Political Bias in Fact-Checking through X-phemisms
Charlott Jakob, David Harbecke, Patrick Parschan +2
Large Language Models are increasingly used in applications requiring objective assessment, which could be compromised by political bias. Many studies found preferences for left-le…
Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution
Tatiana Anikina, Arne Binder, David Harbecke +5
In this work, we reimagine classical probing to evaluate knowledge transfer from simple source to more complex target tasks. Instead of probing frozen representations from a comple…
Evaluating the Robustness of Adverse Drug Event Classification Models Using Templates
Dorothea MacPhail, David Harbecke, Lisa Raithel +1
An adverse drug effect (ADE) is any harmful event resulting from medical drug treatment. Despite their importance, ADEs are often under-reported in official channels. Some research…
Multilingual Relation Classification via Efficient and Effective Prompting
Yuxuan Chen, David Harbecke, Leonhard Hennig
Prompting pre-trained language models has achieved impressive performance on various NLP tasks, especially in low data regimes. Despite the success of prompting in monolingual sett…
Why only Micro-F1? Class Weighting of Measures for Relation Classification
David Harbecke, Yuxuan Chen, Leonhard Hennig +1
Relation classification models are conventionally evaluated using only a single measure, e.g., micro-F1, macro-F1 or AUC. In this work, we analyze weighting schemes, such as micro…
Explaining Natural Language Processing Classifiers with Occlusion and Language Modeling
David Harbecke
Deep neural networks are powerful statistical learners. However, their predictions do not come with an explanation of their process. To analyze these models, explanation methods ar…