Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models
arXiv:2602.23197v2 Announce Type: replace Abstract: Transformer-based large language models exhibit in-context learning, enabling adaptation to downstream tasks via few-shot prompting with demonstrations. In practice, such models are often fine-tuned to improve zero-shot performance on downstream tasks, allowing them to solve tasks without examples and thereby reducing inference costs. However, fine-tuning can degrade in-context learning, limiting the performance of fine-tuned models on tasks not seen during fine-tuning. Using linear attention models, we provide a theoretical analysis that cha
This research addresses a critical challenge in current AI large language model development, where fine-tuning for specific tasks often diminishes broader in-context learning capabilities.
Understanding and addressing the 'forgetting' phenomenon during fine-tuning is crucial for optimizing AI model efficiency, generality, and reducing operational costs across diverse applications.
New theoretical insights into mitigating fine-tuning's negative effects on in-context learning could lead to more robust and versatile AI models that require less frequent and costly retraining.
- · AI developers
- · Cloud providers
- · Enterprises deploying AI
- · Researchers in machine learning
- · AI companies reliant on frequent, costly retraining
Improved fine-tuning techniques will lead to more efficient and capable large language models.
Enhanced model versatility could accelerate AI adoption in new domains and reduce barriers for smaller enterprises.
A foundational shift in general-purpose AI model development, potentially reducing the need for model-specific customization.
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Read at arXiv cs.CL