12 citations · 12 across the 18 of their papers we have counts for
7 papers · 1 filter
Don't Forget! Decomposing the Training Dynamics of Memorization in Language Models
Florian Eichin, Philipp Mondorf, Andrei Mircea +3
Memorization has been proposed as a mechanism to explain how language models fit the tail of their training distributions, but its training dynamics are not understood well. In thi…
When Do Explanations Help In-Context Learning? A Comparative Study of Natural Language Explanation Types and Faithfulness
Mahdi Dhaini, Adam Dejl, Juraj Vladika +3
Natural language explanations (NLEs) are increasingly used as inputs, for example, as few-shot rationales that influence model behavior in in-context learning (ICL). However, it re…
BavGround: A Benchmark for Regional Cultural Grounding and Dialect Competence in Bavarian
Jophin John, Michael Hoffmann, Jan Fillies +2
Cultural evaluation of large language models (LLMs) often focuses on high-resource standard languages, leaving regional culture and dialect communities underrepresented. We introdu…
AIMO Interpretability Challenge
Michal Štefánik, Philipp Mondorf, Andreas Waldis +15
We propose the AIMO Interpretability Challenge, a competition on distinguishing robust from spurious reasoning in frontier mathematical language models based on the models' interna…
Reasoning that Travels: Dissecting How Chain-of-Thought Transfers Across Models
Xinyuan Cheng, Beiduo Chen, Philipp Mondorf +1
Large reasoning models (LRMs) often generate extensive chain-of-thought (CoT) traces before producing a final answer. As explicit textual artifacts, these traces can be passed to o…
Tracing Uncertainty in Language Model "Reasoning"
Nils Grünefeld, Bertram Højer, Philipp Mondorf +5
Language model (LM) "reasoning", commonly described as Chain-of-Thought or test-time scaling, often improves benchmark performance, but the dynamics underlying this process remain…
LogicSkills: A Structured Benchmark for Formal Reasoning in Large Language Models
Brian Rabern, Philipp Mondorf, Barbara Plank
Large language models perform well on many logical reasoning benchmarks, but it remains unclear which core logical skills they truly master. To address this, we introduce LogicSkil…