Pre-Training to Learn in Context
Yuxian Gu Li Dong Furu Wei Minlie Huang
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.