12 citations · 12 across the 18 of their papers we have counts for
7 papers · 1 filter
If Probable, Then Acceptable? Understanding Conditional Acceptability Judgments in Large Language Models
Jasmin Orth, Philipp Mondorf, Barbara Plank
Conditional acceptability refers to how plausible a conditional statement is perceived to be. It plays an important role in communication and reasoning, as it influences how indivi…
What's the Difference? Supporting Users in Identifying the Effects of Prompt and Model Changes Through Token Patterns
Michael A. Hedderich, Anyi Wang, Raoyuan Zhao +3
Prompt engineering for large language models is challenging, as even small prompt perturbations or model changes can significantly impact the generated output texts. Existing evalu…
MAKIEval: A Multilingual Automatic WiKidata-based Framework for Cultural Awareness Evaluation for LLMs
Raoyuan Zhao, Beiduo Chen, Barbara Plank +1
Large language models (LLMs) are used globally across many languages, but their English-centric pretraining raises concerns about cross-lingual disparities for cultural awareness,…
ExPLAIND: Unifying Model, Data, and Training Attribution to Study Model Behavior
Florian Eichin, Yupei Du, Philipp Mondorf +3
Post-hoc interpretability methods typically attribute a model's behavior to its components, data, or training trajectory in isolation, and are often tied to a particular level of g…
Compositional-ARC: Assessing Systematic Generalization in Abstract Spatial Reasoning
Philipp Mondorf, Shijia Zhou, Monica Riedler +1
Systematic generalization refers to the capacity to understand and generate novel combinations from known components. Despite recent progress by large language models (LLMs) across…
Probing LLMs for Multilingual Discourse Generalization Through a Unified Label Set
Florian Eichin, Yang Janet Liu, Barbara Plank +1
Discourse understanding is essential for many NLP tasks, yet most existing work remains constrained by framework-dependent discourse representations. This work investigates whether…
The Validation Gap: A Mechanistic Analysis of How Language Models Compute Arithmetic but Fail to Validate It
Leonardo Bertolazzi, Philipp Mondorf, Barbara Plank +1
The ability of large language models (LLMs) to validate their output and identify potential errors is crucial for ensuring robustness and reliability. However, current research ind…