CURE disentangles LLM recommendation circuits into forget-specific, retain-specific, and task-shared modules with tailored update rules to achieve more effective unlearning than weighted baselines.
arXiv preprint arXiv:2508.07172 , year=
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DualSelect couples task and reference selection via a minimax framework with entropy-regularized scoring to preserve safety in LLM fine-tuning, reporting at least 5.10 point gains in Safety Avg. over baselines on 1B-8B models.
SPARD defends LLMs from harmful fine-tuning attacks via alternating safety projections and relevance-diversity DPP data selection, reporting lowest attack success rates on GSM8K and OpenBookQA while keeping task accuracy.
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CURE:Circuit-Aware Unlearning for LLM-based Recommendation
CURE disentangles LLM recommendation circuits into forget-specific, retain-specific, and task-shared modules with tailored update rules to achieve more effective unlearning than weighted baselines.
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Two to Tango: Coupled Task-Reference Selection for Safe LLM Fine-tuning
DualSelect couples task and reference selection via a minimax framework with entropy-regularized scoring to preserve safety in LLM fine-tuning, reporting at least 5.10 point gains in Safety Avg. over baselines on 1B-8B models.
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SPARD: Defending Harmful Fine-Tuning Attack via Safety Projection with Relevance-Diversity Data Selection
SPARD defends LLMs from harmful fine-tuning attacks via alternating safety projections and relevance-diversity DPP data selection, reporting lowest attack success rates on GSM8K and OpenBookQA while keeping task accuracy.