Papers by Markus Bayer
ActiveLLM: Large Language Model-Based Active Learning for Textual Few-Shot Scenarios (2026.tacl-1)
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| Challenge: | Active learning strategies struggle with a ‘cold-start’ problem, needing substantial initial data to be effective. |
| Approach: | They propose an active learning approach that leverages Large Language Models such as GPT-4, o1, Llama 3, or Mistral Large for selecting instances. |
| Outcome: | The proposed approach outperforms existing methods ADAPET, PERFECT, and SetFit in few-shot scenarios and can be extended to non-few scenarios. |
XAI-Attack: Utilizing Explainable AI to Find Incorrectly Learned Patterns for Black-Box Adversarial Example Creation (2024.lrec-main)
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| Challenge: | Adversarial examples can be used to trick machine learning models into making erroneous predictions, causing poorer insights and lower confidence in the information gathered. |
| Approach: | They propose a textual adversarial example method that identifies falsely learned word indicators by leveraging explainable AI methods as importance functions on incorrectly predicted instances. |
| Outcome: | The proposed method outperforms existing examples and training methods and shows baseline improvements of up to 23 percentage points on adversarial tasks. |