Does Unlearning Survive Collaboration? Robust Unlearning for Shared-Model LLM Teams
We find that collaboration weakens forgetting: final-answer evaluations miss recoverable evidence in team messages, including complementary clues supporting joint recovery.
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Key points
- LLM unlearning is essential for removing sensitive or unwanted knowledge while preserving useful capabilities.
- As LLM teams are increasingly used to solve complex problems, forgetting must remain effective during collaboration.
- Despite advances in forgetting effectiveness, efficiency, and robustness to relearning, team deployment remains underexplored.
- To address this challenge, we propose Aggregation-Conditioned Unlearning (ACU).
Sources (1)
- [1]Does Unlearning Survive Collaboration? Robust Unlearning for Shared-Model LLM TeamsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 03:33 AM
We find that collaboration weakens forgetting: final-answer evaluations miss recoverable evidence in team messages, including complementary clues supporting joint recovery.
LLM unlearning is essential for removing sensitive or unwanted knowledge while preserving useful capabilities.
Extractive summary: sentences quoted from the sources.
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