5 citations · 6 across the 8 of their papers we have counts for
6 papers · 1 filter
Existing Large Language Model Unlearning Evaluations Are Inconclusive
Zhili Feng, Yixuan Even Xu, Alexander Robey +5
Machine unlearning aims to remove sensitive or undesired data from large language models. However, recent studies suggest that unlearning is often shallow, claiming that removed kn…
Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws
Yiding Jiang, Allan Zhou, Zhili Feng +2
The composition of pretraining data is a key determinant of foundation models' performance, but there is no standard guideline for allocating a limited computational budget across…
Rethinking LLM Memorization through the Lens of Adversarial Compression
Avi Schwarzschild, Zhili Feng, Pratyush Maini +2
Large language models (LLMs) trained on web-scale datasets raise substantial concerns regarding permissible data usage. One major question is whether these models "memorize" all th…
An Axiomatic Approach to Model-Agnostic Concept Explanations
Zhili Feng, Michal Moshkovitz, Dotan Di Castro +1
Concept explanation is a popular approach for examining how human-interpretable concepts impact the predictions of a model. However, most existing methods for concept explanations…
TOFU: A Task of Fictitious Unlearning for LLMs
Pratyush Maini, Zhili Feng, Avi Schwarzschild +2
Large language models trained on massive corpora of data from the web can memorize and reproduce sensitive or private data raising both legal and ethical concerns. Unlearning, or t…
On the Neural Tangent Kernel of Equilibrium Models
Zhili Feng, J. Zico Kolter
This work studies the neural tangent kernel (NTK) of the deep equilibrium (DEQ) model, a practical ``infinite-depth'' architecture which directly computes the infinite-depth limit…