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cs.AI2025
Is Model Editing Built on Sand? Revealing Its Illusory Success and Fragile Foundation
Wei Liu, Haomei Xu, Bingqing Liu +6
Large language models (LLMs) inevitably encode outdated or incorrect knowledge. Updating, deleting, and forgetting such knowledge is important for alignment, safety, and other issu…
cs.AI2025
Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets
Wei Liu, Zhongyu Niu, Lang Gao +4
This study investigates the self-rationalization framework constructed with a cooperative game, where a generator initially extracts the most informative segment from raw input, an…
cs.AI2025★ 1 cited
Breaking Free from MMI: A New Frontier in Rationalization by Probing Input Utilization
Wei Liu, Zhiying Deng, Zhongyu Niu +4
Extracting a small subset of crucial rationales from the full input is a key problem in explainability research. The most widely used fundamental criterion for rationale extraction…