We applied few-shot in-context learning on the OPT-1.3B model for the natural language inference task and employed knowledge distillation to internalize the context information, reducing model parameter from 1.3B to 125M and achieving a size reduction from 2.5GB to 0.25GB. Compared to using in-context learning alone on similarly sized models, this context distillation approach achieved a nearly 50% improvement in out-of-domain accuracy, demonstrating superior knowledge transfer capabilities over prompt-based methods. Furthermore, this approach reduced memory consumption by up to 60% while delivering a 20% improvement in out-of-domain accuracy compared to conventional pattern-based fine-tuning.
@article{arxiv.2412.13243,
title = {In-Context Learning Distillation for Efficient Few-Shot Fine-Tuning},
author = {Yifei Duan and Liu Li and Zirui Zhai and Jinxia Yao},
journal= {arXiv preprint arXiv:2412.13243},
year = {2024}
}