FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning

• URL: http://arxiv.org/abs/2309.04663v2
• Date: Tue, 12 Sep 2023 14:34:03 GMT
• Title: FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning
• Authors: Xinyi Wang, John Wieting, Jonathan H. Clark
• Abstract summary: We propose a new learning paradigm called FIAT that fuses the best of ICL and fine-tuning paradigms together. 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.
• Score: 54.48012868892613
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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