FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning
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.
64ffc9535d763775629dad3d
Xinyi Wang
64ffc9535d763775629dad3e
John Wieting
admin_assigned
2023-09-12T12:21:03.929Z
623d449d38540758f05ccfc4
/avatars/d79808850b1a2ef5fe23e08eb6f22d57.svg
John Wieting
user
jwieting
64ffc9535d763775629dad3f
Jonathan H. Clark
admin_assigned
2023-09-12T12:21:29.174Z
6244a009a37b7efa7d335e47
/avatars/465c97aa99e0377b09e2f43813da2701.svg
Jonathan Clark
user
jhclark
FIAT: Fusing learning paradigms with Instruction-Accelerated Tuning
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.
FIAT, a new learning paradigm, combines in-context learning and fine-tuning to improve performance across multilingual tasks using different model sizes.
in-context learning fine-tuning FIAT prompt-engineering chain-of-thought reasoning parameter updates parameter-efficient tuning multilingual tasks