565 citations · 581 across the 9 of their papers we have counts for
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cs.LG2024★ 4 cited
Rethinking Machine Unlearning for Large Language Models
Sijia Liu, Yuanshun Yao, Jinghan Jia +11
We explore machine unlearning (MU) in the domain of large language models (LLMs), referred to as LLM unlearning. This initiative aims to eliminate undesirable data influence (e.g.,…
cs.CL2024
The Unreasonable Effectiveness of Easy Training Data for Hard Tasks
Peter Hase, Mohit Bansal, Peter Clark +1
How can we train models to perform well on hard test data when hard training data is by definition difficult to label correctly? This question has been termed the scalable oversigh…