3 papers
cs.LG2026
LLM generation novelty through the lens of semantic similarity
Philipp Davydov, Ameya Prabhu, Matthias Bethge +2
Generation novelty is a key indicator of an LLM's ability to generalize, yet measuring it against full pretraining corpora is computationally challenging. Existing evaluations ofte…
cs.HC2025
Towards User-Focused Research in Training Data Attribution for Human-Centered Explainable AI
Elisa Nguyen, Johannes Bertram, Evgenii Kortukov +2
Explainable AI (XAI) aims to make AI systems more transparent, yet many practices emphasise mathematical rigour over practical user needs. We propose an alternative to this model-c…
cs.LG2025
Better Training Data Attribution via Better Inverse Hessian-Vector Products
Andrew Wang, Elisa Nguyen, Runshi Yang +3
Training data attribution (TDA) provides insights into which training data is responsible for a learned model behavior. Gradient-based TDA methods such as influence functions and u…