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You Only Fine-tune Once: Many-Shot In-Context Fine-Tuning for Large Language Models
🤖AI Summary
Researchers propose Many-Shot In-Context Fine-tuning (ManyICL), a novel approach that significantly improves large language model performance by treating multiple in-context examples as supervised training targets rather than just prompts. The method narrows the performance gap between in-context learning and dedicated fine-tuning while reducing catastrophic forgetting issues.
Key Takeaways
- →ManyICL extends in-context learning to a many-shot setting, treating every answer within context as a supervised training target.
- →The approach significantly outperforms zero/few-shot fine-tuning and approaches dedicated fine-tuning performance levels.
- →ManyICL effectively mitigates catastrophic forgetting issues commonly observed in traditional fine-tuning methods.
- →Experiments demonstrate effectiveness across diverse tasks including classification, summarization, question answering, and mathematics.
- →The method enables moderately sized models like Mistral 7B and Llama-3 8B to handle multiple downstream tasks simultaneously.
#large-language-models#fine-tuning#in-context-learning#machine-learning#ai-training#llm-optimization#mistral#llama#gemma
Read Original →via arXiv – CS AI
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