REGLU guides LoRA-based unlearning via representation subspaces and orthogonal regularization to outperform prior methods on forget-retain trade-off in LLM benchmarks.
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RASP-Tuner matches or beats GP-UCB and CMA-ES regret on seven of nine synthetic non-stationary tasks while running 8-12 times faster per step.
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Representation-Guided Parameter-Efficient LLM Unlearning
REGLU guides LoRA-based unlearning via representation subspaces and orthogonal regularization to outperform prior methods on forget-retain trade-off in LLM benchmarks.
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RASP-Tuner: Retrieval-Augmented Soft Prompts for Context-Aware Black-Box Optimization in Non-Stationary Environments
RASP-Tuner matches or beats GP-UCB and CMA-ES regret on seven of nine synthetic non-stationary tasks while running 8-12 times faster per step.