3 citations · 5 across the 3 of their papers we have counts for
3 papers
Understanding Memorisation in LLMs: Dynamics, Influencing Factors, and Implications
Till Speicher, Mohammad Aflah Khan, Qinyuan Wu +5
Understanding whether and to what extent large language models (LLMs) have memorised training data has important implications for the reliability of their output and the privacy of…
Understanding the Role of Invariance in Transfer Learning
Till Speicher, Vedant Nanda, Krishna P. Gummadi
Transfer learning is a powerful technique for knowledge-sharing between different tasks. Recent work has found that the representations of models with certain invariances, such as…
What Happens During Finetuning of Vision Transformers: An Invariance Based Investigation
Gabriele Merlin, Vedant Nanda, Ruchit Rawal +1
The pretrain-finetune paradigm usually improves downstream performance over training a model from scratch on the same task, becoming commonplace across many areas of machine learni…