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Notes on unlearning
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GUARD: Natural Forgetting in Large Reasoning Models via Guided Answer-Reasoning Distillation
GUARD frames unlearning in large reasoning models as learning a coherent non-disclosing reasoning trajectory followed by a stable refusal-style answer. It uses Guided Answer-Reasoning Distillation and introduces…
Unlearning Under Imbalance: Benchmarking Fairness in Multimodal LLM Unlearning
Researchers propose FAIRGET, a Visual Question Answering benchmark to evaluate the effectiveness of unlearning (removing personal data from trained models) in multimodal large language models under unbalanced…
Unlearning Under Imbalance: Benchmarking Fairness in Multimodal LLM Unlearning
Researchers propose a new benchmark, FAIRGET, to evaluate the effectiveness of machine unlearning in multimodal large language models under unbalanced, realistic scenarios. They also introduce FAUN, an unlearning…
LACUNA: A Testbed for Evaluating Localization Precision for LLM Unlearning
Researchers introduce a new testbed called LACUNA that allows for the evaluation of unlearning methods at the parameter level, rather than just the output level. They find that current state-of-the-art (SOTA)…
LACUNA: A Testbed for Evaluating Localization Precision for LLM Unlearning
Researchers introduce LACUNA, a testbed for evaluating the precision of unlearning methods in large language models (LLMs). The testbed allows direct evaluation of whether unlearning targets the weights responsible for…
LACUNA: A Testbed for Evaluating Localization Precision for LLM Unlearning
Researchers introduce LACUNA, a testbed that allows for direct evaluation of unlearning methods at the parameter level. LACUNA is used to assess current state-of-the-art unlearning methods and finds them to be…
LACUNA: A Testbed for Evaluating Localization Precision for LLM Unlearning
The researchers introduce a new testbed called LACUNA for evaluating the precision of post hoc removal methods (unlearning) for LLMs. The testbed focuses on injecting personal identifiable information into specific…
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