12 citations · 12 across the 18 of their papers we have counts for
5 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…
LPDS: Evaluating LLM Robustness Through Logic-Preserving Difficulty Scaling
Philipp Mondorf, Samuel J. Bell, Jesse Dodge +1
As large language models (LLMs) are increasingly deployed to perform tasks with minimal human oversight, it is crucial that these models operate robustly. In particular, a model th…
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…
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…
Circuit Compositions: Exploring Modular Structures in Transformer-Based Language Models
Philipp Mondorf, Sondre Wold, Barbara Plank
A fundamental question in interpretability research is to what extent neural networks, particularly language models, implement reusable functions through subnetworks that can be co…