NPO enables stable unlearning of 50%+ training data in LLMs on TOFU by making collapse exponentially slower than gradient ascent, preserving sensible outputs where prior methods fail.
Large language model unlearning
8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
DivIn samples initial noise from a guidance potential posterior via Langevin dynamics to improve diversity in class-to-image and text-to-image generation.
Probe-geometry alignment erases cross-sequence memorization signatures in LLMs below chance using per-depth rank-one activation interventions with negligible impact on zero-shot capabilities.
Non-model gains via inference, systems, and assets can drive AI capabilities independently of base models, requiring governance beyond model-level evaluation and mitigation.
REGLU guides LoRA-based unlearning via representation subspaces and orthogonal regularization to outperform prior methods on forget-retain trade-off in LLM benchmarks.
OFMU is a penalty-based bi-level optimizer for machine unlearning that alternates between a gradient-ascent forgetting step and a gradient-descent utility-restoration step, with a similarity penalty between forget and retain gradients.
MSA performs data unlearning in LLMs by arithmetic operations on prior model checkpoints to remove targeted datapoint influence, with experiments showing competitive or better results than existing unlearning methods.
RPO-PDT demonstrates a role-play-based, retrieval-grounded system for adaptive, policy-constrained student support dialogue with reverse-roleplay for strategy memory.
citing papers explorer
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Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning
NPO enables stable unlearning of 50%+ training data in LLMs on TOFU by making collapse exponentially slower than gradient ascent, preserving sensible outputs where prior methods fail.
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Initialization is Half the Battle: Generating Diverse Images from a Guidance Potential Posterior
DivIn samples initial noise from a guidance potential posterior via Langevin dynamics to improve diversity in class-to-image and text-to-image generation.
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Probe-Geometry Alignment: Erasing the Cross-Sequence Memorization Signature Below Chance
Probe-geometry alignment erases cross-sequence memorization signatures in LLMs below chance using per-depth rank-one activation interventions with negligible impact on zero-shot capabilities.
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Comprehensive AI governance requires addressing non-model gains
Non-model gains via inference, systems, and assets can drive AI capabilities independently of base models, requiring governance beyond model-level evaluation and mitigation.
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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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OFMU: Optimization-Driven Framework for Machine Unlearning
OFMU is a penalty-based bi-level optimizer for machine unlearning that alternates between a gradient-ascent forgetting step and a gradient-descent utility-restoration step, with a similarity penalty between forget and retain gradients.
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Revisiting the Past: Data Unlearning with Model State History
MSA performs data unlearning in LLMs by arithmetic operations on prior model checkpoints to remove targeted datapoint influence, with experiments showing competitive or better results than existing unlearning methods.
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RPO-PDT: Demonstrating Role-Play-Based Knowledge Adaptation for Student Support Dialogue (Demonstration System)
RPO-PDT demonstrates a role-play-based, retrieval-grounded system for adaptive, policy-constrained student support dialogue with reverse-roleplay for strategy memory.