NADS: Navigator-Guided Data Selection for Mitigating Catastrophic Forgetting in Fine-Tuning
Catastrophic forgetting is a central obstacle in single-stage fine-tuning of large language models (LLMs) on a new task, especially when the original pretraining data are unavailable.
ProofPaper ↗
Key points
- Synthetic data offers a practical mitigation path, but candidate pools are often noisy and redundant, making effective selection essential.
- To address these challenges, we propose NAvigator-guided Data Selection (NADS), a fine-tuning framework for LLMs. NADS first follows the target fine-tuning recipe without preservation regularization to obtain a navigator model, whose deviation from the pretrained model exposes recipe-induced capability drift.
- It then uses the predictive divergence between the navigator and the pretrained model to score forgetting-aware utility for each synthetic candidate, and constructs a constraint set through an efficient utility--diversity selection strategy.
- Experiments show that NADS delivers a stronger balance between new-task performance and general capability preservation, while reducing utility--diversity selection overhead.
Sources (1)
- [1]NADS: Navigator-Guided Data Selection for Mitigating Catastrophic Forgetting in Fine-TuningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 04:02 AM
Catastrophic forgetting is a central obstacle in single-stage fine-tuning of large language models (LLMs) on a new task, especially when the original pretraining data are unavailable.
Synthetic data offers a practical mitigation path, but candidate pools are often noisy and redundant, making effective selection essential.
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