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cs.CL2025
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…
cs.CL2023
Understanding and Mitigating Classification Errors Through Interpretable Token Patterns
Michael A. Hedderich, Jonas Fischer, Dietrich Klakow +1
State-of-the-art NLP methods achieve human-like performance on many tasks, but make errors nevertheless. Characterizing these errors in easily interpretable terms gives insight int…