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arXiv · 2310.05914

NEFTune: Noisy Embeddings Improve Instruction Finetuning

Neel Jain·Ping-yeh Chiang·Yuxin Wen·John Kirchenbauer·Hong-Min Chu·Gowthami Somepalli·Brian R. Bartoldson·Bhavya Kailkhura·Avi Schwarzschild·Aniruddha Saha·Micah Goldblum·Jonas Geiping·Tom Goldstein
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Abstract

We show that language model finetuning can be improved, sometimes dramatically, with a simple augmentation. NEFTune adds noise to the embedding vectors during training. Standard finetuning of LLaMA-2-7B using Alpaca achieves 29.79% on AlpacaEval, which rises to 64.69% using noisy embeddings. NEFTune also improves over strong baselines on modern instruction datasets. Models trained with Evol-Instruct see a 10% improvement, with ShareGPT an 8% improvement, and with OpenPlatypus an 8% improvement. Even powerful models further refined with RLHF such as LLaMA-2-Chat benefit from additional training with NEFTune.

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Neel Jain, Ping-yeh Chiang, Yuxin Wen, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R. Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein. 2023-10-09. NEFTune: Noisy Embeddings Improve Instruction Finetuning. https://arxiv.org/abs/2310.05914

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