FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning
This provides a practical solution for leveraging LLMs without choosing between learning paradigms, though it appears incremental as it builds on existing methods.
The authors tackled the trade-offs between in-context learning and fine-tuning for large language models by proposing FIAT, a new paradigm that fuses both approaches, resulting in better performance than either method alone across multilingual tasks with 100-10,000 training examples.
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.