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README.md

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+# Adapting Large Language Models via Reading Comprehension
+
+This repo contains the model for our paper [Adapting Large Language Models via Reading Comprehension](https://arxiv.org/pdf/2309.09530.pdf)
+
+We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in **biomedicine, finance, and law domains**. Our 7B model competes with much larger domain-specific models like BloombergGPT-50B. Moreover, our domain-specific reading comprehension texts enhance model performance even on general benchmarks, indicating potential for developing a general LLM across more domains.
+
+## GitHub repo: 
+https://github.com/microsoft/LMOps
+
+## Domain-specific LLMs:
+Our models of different domains are now available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are:
+
+<p align='center'>
+    <img src="./comparison.png" width="700">
+</p>
+
+## Domain-specific Tasks:
+To easily reproduce our results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
+
+
+## Citation:
+```bibtex
+@inproceedings{AdaptLLM,
+    title={Adapting Large Language Models via Reading Comprehension}, 
+    author={Daixuan Cheng and Shaohan Huang and Furu Wei},
+    url={https://arxiv.org/abs/2309.09530},
+    year={2023},
+}
+```
+