Papers by Henrike Beyer
Natural Language Reasoning in Large Language Models: Analysis and Evaluation (2025.findings-acl)
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| Challenge: | Argumentative reasoning presents unique challenges due to its reliance on context, implicit assumptions, and value judgments. |
| Approach: | They propose a large-scale evaluation of LLMs' unconstrained natural language reasoning capabilities . they formalise a new strategy designed to evaluate argumentative reasoning in LLM . |
| Outcome: | The proposed model performs better on a range of reasoning tasks than other models. |
Lexical Recall or Logical Reasoning: Probing the Limits of Reasoning Abilities in Large Language Models (2025.acl-long)
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| Challenge: | Existing work on LLMs assesses logic abilities independently from lexical memory. |
| Approach: | They propose to assess LLMs' logic abilities independently from lexical memory . they use two sets of grid puzzles in 42 different sizes and 12 difficulty levels . |
| Outcome: | The proposed benchmarks show that LLMs are limited to a few steps of reasoning . the results show that the applied obfuscation strategies help mitigate effects of logic puzzles being part of training data. |
Linguistic Features in German BERT: The Role of Morphology, Syntax, and Semantics in Multi-Class Text Classification (2025.naacl-srw)
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| Challenge: | a monolingual German BERT model is used for semantic classification of newspaper articles . linguistic features identified in English affect classification in German, but suggest important language- and task-specific features as well. |
| Approach: | They examine a monolingual German BERT model using a semantic classification task on newspaper articles. |
| Outcome: | The proposed model uses the TüBa-D/Z corpus, a resource with gold-standard annotations for linguistic features. |