Adapting Large Language Models via Reading Comprehension
Authors: name: Daixuan Cheng, name: Shaohan Huang, name: Furu Wei

We explore how continued pre-training on domain-specific corpora influences
large language models, revealing that training on the raw corpora endows the
model with domain knowledge, but drastically hurts its prompting ability for
question answering. Taken inspiration from human learning via reading
comprehension--practice after reading improves the ability to answer questions
based on the learned knowledge--we propose a simple method for transforming raw
corpora into reading comprehension texts. Each raw text is enriched with a
series of tasks related to its content. Our method, highly scalable and
applicable to any pre-training corpora, consistently enhances performance
across various tasks in three different domains: biomedicine, finance, and law.
Notably, our 7B language model achieves competitive performance with
domain-specific models of much larger scales, such as BloombergGPT-50B.
Furthermore, we demonstrate that domain-specific reading comprehension texts
can improve the model's performance even on general benchmarks, showing the
potential to develop a general model across even more domains. Our model, code,
and data will be available at https://github.com/microsoft/LMOps.