FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning
Authors: name: Xinyi Wang, name: John Wieting, name: Jonathan H. Clark

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.