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AI Research
arXiv CS.AI·May 25·8 min read
Understanding and Improving Noisy Embedding Techniques in Instruction Finetuning
TL;DR
Prior work (NEFTune) added uniform noise to embeddings during language model fine-tuning but lacked theoretical justification for why uniform noise outperformed Gaussian noise. Researchers developed SymNoise, a new fine-tuning method using symmetric noise in embeddings to better control local curvature.
✦ Why It Matters
Engineers can adopt SymNoise to achieve measurably better instruction-following performance in fine-tuned language models with minimal implementation overhead.
Key Takeaways
- Explore using SymNoise for improved fine-tuning results.
- Test symmetric noise in your language model training.
- Compare performance metrics against NEFTune.
- Investigate the impact of noise types on model accuracy.
How It Works
SymNoise enhances fine-tuning by introducing symmetric noise into embeddings, which helps regulate the model's local curvature more effectively than previous methods. This regulation allows the model to learn more robust representations, leading to improved performance on tasks.