ReFT: Representation Finetuning for Language Models
Parameter-efficient fine-tuning (PEFT) methods seek to adapt large models via
updates to a small number of weights. However, much prior interpretability work
has shown that representations encode rich semantic information, suggesting
that editing representations might be a more powerful alternative. Here, we
pursue this hypothesis by developing a family of Representation
Finetuning (ReFT) methods. ReFT methods operate on a frozen base model and
learn task-specific interventions on hidden representations. We define a strong
instance of the ReFT family, Low-rank Linear Subspace ReFT (LoReFT). LoReFT is
a drop-in replacement for existing PEFTs and learns interventions that are
10x-50x more parameter-efficient than prior state-of-the-art PEFTs. We showcase
LoReFT on eight commonsense reasoning tasks, four arithmetic reasoning tasks,
Alpaca-Eval v1.0, and GLUE. In all these evaluations, LoReFT delivers the best
balance of efficiency and performance, and almost always outperforms
state-of-the-art PEFTs. We release a generic ReFT training library publicly at
https://github.com/stanfordnlp/pyreft.
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Zhengxuan Wu
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2024-04-06T15:19:32.200Z
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Zhengxuan Wu
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Aryaman Arora
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2024-04-05T10:14:26.373Z
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Aryaman Arora
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Zheng Wang
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Atticus Geiger
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2024-05-27T07:21:07.672Z
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Atticus Geiger
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Dan Jurafsky
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Christopher D. Manning
admin_assigned
2024-04-25T07:37:50.983Z
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Christopher Manning
user
manning
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Christopher Potts
ReFT: Representation Finetuning for Language Models
Parameter-efficient fine-tuning (PEFT) methods seek to adapt large models via
updates to a small number of weights. However, much prior interpretability work
has shown that representations encode rich semantic information, suggesting
that editing representations might be a more powerful alternative. Here, we
pursue this hypothesis by developing a family of Representation
Finetuning (ReFT) methods. ReFT methods operate on a frozen base model and
learn task-specific interventions on hidden representations. We define a strong
instance of the ReFT family, Low-rank Linear Subspace ReFT (LoReFT). LoReFT is
a drop-in replacement for existing PEFTs and learns interventions that are
10x-50x more parameter-efficient than prior state-of-the-art PEFTs. We showcase
LoReFT on eight commonsense reasoning tasks, four arithmetic reasoning tasks,
Alpaca-Eval v1.0, and GLUE. In all these evaluations, LoReFT delivers the best
balance of efficiency and performance, and almost always outperforms
state-of-the-art PEFTs. We release a generic ReFT training library publicly at
https://github.com/stanfordnlp/pyreft.
Representation Finetuning (ReFT) methods, exemplified by Low-rank Linear Subspace ReFT (LoReFT), achieve high efficiency and performance by adapting representations in frozen base models, outperforming state-of-the-art Parameter-efficient Fine-tuning (PEFT) methods.
Parameter-efficient fine-tuning Representation Finetuning ReFT Low-rank Linear Subspace ReFT LoReFT hidden representations commonsense reasoning arithmetic reasoning Alpaca-Eval GLUE