4 citations · 8 across the 16 of their papers we have counts for
4 papers · 1 filter
TULIP: Targeted LLM Unlearning at Layers Identified Per-input
Yejin Kim, William F. Shen, Seokwon Jung +2
Representation-level unlearning modifies the intermediate hidden states of LLMs. Although knowledge is distributed across layers, existing methods operate at a single fixed layer f…
How LLM Task-Adaptation Reshapes Alignment: A Multi-dimensional Study of Behavioral and Representational Drift
James Elcock, William F. Shen, Xinchi Qiu +1
Post-training is a key mechanism for adapting large language models to downstream tasks. While prior work suggests that task adaptation can alter a model's pre-existing alignment,…
SEAT: Sparse Entity-Aware Tuning for Knowledge Adaptation while Preserving Epistemic Abstention
William F. Shen, Xinchi Qiu, Nicola Cancedda +1
Adapting LLMs with new knowledge is increasingly important, but standard fine-tuning often erodes aligned epistemic abstention: the ability to acknowledge when the model does not k…
Compliance Cards: Automated EU AI Act Compliance Analyses amidst a Complex AI Supply Chain
Bill Marino, Yaqub Chaudhary, Yulu Pi +6
As the AI supply chain grows more complex, AI systems and models are increasingly likely to incorporate multiple internally- or externally-sourced components such as datasets and (…