| Challenge: | Existing methods for large language models suffer from two major issues: in-domain data are scarce compared with general domain-agnostic data. |
| Approach: | They propose a task-oriented in-domain data augmentation framework that uses in- domain data selection and task-orientated synthetic passage generation to adapt LLMs to two domains: advertisement and math. |
| Outcome: | The proposed framework improves LLM performance by 8% in the advertisement domain and 7.5% in the math domain. |
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| Challenge: | True. True. False |
| Approach: | False slants are proposed to generate a large pool of augmentation instructions and select the most suitable task-informed instructions. |
| Outcome: | False omissions: the proposed approach consistently generates augmented data with better quality compared to non-LLM and LLM-based data augmentation methods. |
Systematic Knowledge Injection into Large Language Models via Diverse Augmentation for Domain-Specific RAG (2025.findings-naacl)
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Kushagra Bhushan, Yatin Nandwani, Dinesh Khandelwal, Sonam Gupta, Gaurav Pandey, Dinesh Raghu, Sachindra Joshi
| Challenge: | Retrieval-Augmented Generation (RAG) enhances response relevance by incorporating retrieved domain knowledge in the context, retrieval errors can still lead to hallucinations and incorrect answers. |
| Approach: | They propose a framework that augments the learning process by context augmentation and knowledge paraphrasing by incorporating retrieved domain knowledge into the context. |
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Exploring Data Augmentation for Code Generation Tasks (2023.findings-eacl)
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| Challenge: | Recent advances in natural language processing have impacted how models are trained for programming language tasks. |
| Approach: | They propose to use augmentation methods that yield consistent improvements in code translation and summarization by up to 6.9% and 7.5% respectively. |
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Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey (2025.findings-emnlp)
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| Challenge: | specialized LLMs are often limited in domain-specific applications that require specialized knowledge. |
| Approach: | They provide a comprehensive overview of four key methods to enhance large language models by integrating domain-specific knowledge. |
| Outcome: | The proposed methods are categorized into four key approaches: dynamic knowledge injection, static knowledge embedding, modular adapters, and prompt optimization. |
RAG-Studio: Towards In-Domain Adaptation of Retrieval Augmented Generation Through Self-Alignment (2024.findings-emnlp)
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| Challenge: | Existing RAG systems that use pre-trained LLMs and retrievers often fail in specialized domains and applications. |
| Approach: | They propose a self-aligned training framework that adapts general RAG models to specific domains solely through synthetic data. |
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Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment (2024.emnlp-main)
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| Challenge: | Pre-trained language models have limited generalization capabilities and performance challenges. |
| Approach: | They evaluate 15 different backbone LLMs and non-LLMs to evaluate their performance . larger models and extensive pre-training consistently enhance in-domain accuracy and data efficiency . |
| Outcome: | The results show that larger models and extensive pre-training enhance in-domain accuracy and data efficiency. |
Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks (2020.acl-main)
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Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, Noah A. Smith
| Challenge: | Language models prerained on text from a wide variety of sources form the foundation of today’s NLP. |
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Transfer-Aware Data Selection for Domain Adaptation in Text Retrieval (2025.findings-emnlp)
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| Challenge: | Existing methods to improve domain adaptation do not guarantee improved adaptability, but may negatively impact model performance. |
| Approach: | They propose a framework that can effectively improve model adaptability by selecting beneficial data without evaluating all source data. |
| Outcome: | The proposed framework improves model adaptability by selecting beneficial data without evaluating all source data. |
Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation (2021.acl-long)
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| Challenge: | Existing methods to train pre-trained models require domain-specific data and computational resources. |
| Approach: | They propose a domain-aware N-gram Adaptor to incorporate unseen and domain-specific words into a generic pretrained model. |
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KALA: Knowledge-Augmented Language Model Adaptation (2022.naacl-main)
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| Challenge: | Pre-trained language models (PLMs) have proved to be effective on various natural language understanding tasks. |
| Approach: | They propose a domain adaption framework which modulates the intermediate hidden representations of PLMs with domain knowledge, consisting of entities and their relational facts. |
| Outcome: | The proposed framework outperforms adaptive pre-training on question answering and named entity recognition tasks on multiple datasets across different domains. |