# Adapting Large Language Models to Domains via Reading Comprehension

arXiv: https://arxiv.org/abs/2309.09530 · HTML: https://mlsanity.com/paper/2309.09530/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- Builds on 64 of its 88 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 30 of 64)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019)
- [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://mlsanity.com/paper/1811.00937/) (2018)
- [Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling](https://mlsanity.com/paper/2304.01373/) (2023)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)

## Built on by (most cited first, 30 of 50)

- [A Survey on Knowledge Distillation of Large Language Models](https://mlsanity.com/paper/2402.13116/) (2024)
- [Orca-Math: Unlocking the potential of SLMs in Grade School Math](https://mlsanity.com/paper/2402.14830/) (2024)
- [Continual Learning for Large Language Models: A Survey](https://mlsanity.com/paper/2402.01364/) (2024)
- [SaulLM-7B: A pioneering Large Language Model for Law](https://mlsanity.com/paper/2403.03883/) (2024)
- [LLaMA Pro: Progressive LLaMA with Block Expansion](https://mlsanity.com/paper/2401.02415/) (2024)
- [Guiding Language Model Reasoning with Planning Tokens](https://mlsanity.com/paper/2310.05707/) (2023)
- [Pack of LLMs: Model Fusion at Test-Time via Perplexity Optimization](https://mlsanity.com/paper/2404.11531/) (2024)
- [Instruction-tuned Language Models are Better Knowledge Learners](https://mlsanity.com/paper/2402.12847/) (2024)
- [MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs](https://mlsanity.com/paper/2402.11756/) (2024)
- [Patho-R1: A Multimodal Reinforcement Learning-Based Pathology Expert Reasoner](https://mlsanity.com/paper/2505.11404/) (2025)
- [MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2405.12130/) (2024)
- [Specialist or Generalist? Instruction Tuning for Specific NLP Tasks](https://mlsanity.com/paper/2310.15326/) (2023)
- [BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models](https://mlsanity.com/paper/2403.18365/) (2024)
- [Llama meets EU: Investigating the European Political Spectrum through the Lens of LLMs](https://mlsanity.com/paper/2403.13592/) (2024)
- [Large Language Models for Social Networks: Applications, Challenges, and Solutions](https://mlsanity.com/paper/2401.02575/) (2024)
- [Octopus v4: Graph of language models](https://mlsanity.com/paper/2404.19296/) (2024)
- [Improving Domain Adaptation through Extended-Text Reading Comprehension](https://mlsanity.com/paper/2401.07284/) (2024)
- [Read Before You Think: Mitigating LLM Comprehension Failures with Step-by-Step Reading](https://mlsanity.com/paper/2504.09402/) (2025)
- [Knowledge Fusion of Large Language Models Via Modular SkillPacks](https://mlsanity.com/paper/2505.18502/) (2025)
- [DPPA: Pruning Method for Large Language Model to Model Merging](https://mlsanity.com/paper/2403.02799/) (2024)
- [Mitigating Unintended Memorization with LoRA in Federated Learning for LLMs](https://mlsanity.com/paper/2502.05087/) (2025)
- [Dr.Academy: A Benchmark for Evaluating Questioning Capability in Education for Large Language Models](https://mlsanity.com/paper/2408.10947/) (2024)
- [OntoURL: A Benchmark for Evaluating Large Language Models on Symbolic Ontological Understanding, Reasoning and Learning](https://mlsanity.com/paper/2505.11031/) (2025)
- [textttBluePrint: A Social Media User Dataset for LLM Persona Evaluation and Training](https://mlsanity.com/paper/2510.02343/) (2025)
- [Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models](https://mlsanity.com/paper/2405.12939/) (2024)
- [Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains](https://mlsanity.com/paper/2505.16014/) (2025)
- [Aalap: AI Assistant for Legal & Paralegal Functions in India](https://mlsanity.com/paper/2402.01758/) (2024)
- [Understanding Privacy Risks of Embeddings Induced by Large Language Models](https://mlsanity.com/paper/2404.16587/) (2024)
- [Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse Granularity](https://mlsanity.com/paper/2405.16579/) (2024)
- [Knowledge is Not Enough: Injecting RL Skills for Continual Adaptation](https://mlsanity.com/paper/2601.11258/) (2026)

## Abstract (arXiv)

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 are available at https://github.com/microsoft/LMOps.