Pre-Training to Learn in Context
In-context learning, where pre-trained language models learn to perform tasks
from task examples and instructions in their contexts, has attracted much
attention in the NLP community. However, the ability of in-context learning is
not fully exploited because language models are not explicitly trained to learn
in context. To this end, we propose PICL (Pre-training for In-Context
Learning), a framework to enhance the language models' in-context learning
ability by pre-training the model on a large collection of "intrinsic tasks" in
the general plain-text corpus using the simple language modeling objective.
PICL encourages the model to infer and perform tasks by conditioning on the
contexts while maintaining task generalization of pre-trained models. We
evaluate the in-context learning performance of the model trained with PICL on
seven widely-used text classification datasets and the Super-NaturalInstrctions
benchmark, which contains 100+ NLP tasks formulated to text generation. Our
experiments show that PICL is more effective and task-generalizable than a
range of baselines, outperforming larger language models with nearly 4x
parameters. The code is publicly available at https://github.com/thu-coai/PICL.
64642753e0475a83333dacec
Yuxian Gu
claimed_verified
2023-05-19T10:28:05.554Z
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Yuxian Gu
user
t1101675
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Li Dong
claimed_verified
2024-10-08T15:20:26.184Z
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Li Dong
user
unilm
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Furu Wei
admin_assigned
2023-05-26T09:29:27.910Z
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Furu Wei
user
thegenerality
64642753e0475a83333dacef
Minlie Huang
Pre-Training to Learn in Context
In-context learning, where pre-trained language models learn to perform tasks
from task examples and instructions in their contexts, has attracted much
attention in the NLP community. However, the ability of in-context learning is
not fully exploited because language models are not explicitly trained to learn
in context. To this end, we propose PICL (Pre-training for In-Context
Learning), a framework to enhance the language models' in-context learning
ability by pre-training the model on a large collection of "intrinsic tasks" in
the general plain-text corpus using the simple language modeling objective.
PICL encourages the model to infer and perform tasks by conditioning on the
contexts while maintaining task generalization of pre-trained models. We
evaluate the in-context learning performance of the model trained with PICL on
seven widely-used text classification datasets and the Super-NaturalInstrctions
benchmark, which contains 100+ NLP tasks formulated to text generation. Our
experiments show that PICL is more effective and task-generalizable than a
range of baselines, outperforming larger language models with nearly 4x
parameters. The code is publicly available at https://github.com/thu-coai/PICL.
PICL enhances language models' in-context learning by pre-training on intrinsic tasks, improving performance and generalization across various text classification and task generation benchmarks.
in-context learning pre-training intrinsic tasks language modeling task generalization text classification Super-NaturalInstructions parameter-efficient