FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning

Xinyi Wang, John Wieting, Jonathan Clark

FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning: 6 upvotes on Hugging Face Daily Papers, #8 of 8 papers on 2023-09-12. Day-by-day upvote history.

Learning paradigms for large language models (LLMs) currently tend to fall within either in-context learning (ICL) or full fine-tuning. Each of these comes with their own trade-offs based on available data, model size, compute cost, ease-of-use, and final quality with neither solution performing well across-the-board. In this article, we first describe ICL and fine-tuning paradigms in a way that highlights their natural connections. Based on these connections, we propose a new learning paradigm called FIAT that fuses the best of these paradigms together, enabling prompt-engineered instructions and chain-of-thought reasoning with the very largest models while also using similar methods to perform parameter updates on a modestly-sized LLM with parameter-efficient tuning. We evaluate FIAT's effectiveness on a variety of multilingual tasks and observe that FIAT performs better than both ICL and fine-tuning at scales ranging from 100-10,000 training examples. We hope that FIAT provides a practical way of harnessing the full potential of LLMs without needing to make a hard choice between learning paradigms.

Paper page on Hugging Face · arXiv

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