RAG-Studio: Towards In-Domain Adaptation of Retrieval Augmented Generation Through Self-Alignment (2024.findings-emnlp)
Copied to clipboard
| 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. |
| Outcome: | Experiments on specialized domain corpus, general LLM, and general retriever show that the self-aligned training framework outperforms human-annotated training data in specialized fields. |
Similar Papers
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains (2025.naacl-long)
Copied to clipboard
Ran Xu, Hui Liu, Sreyashi Nag, Zhenwei Dai, Yaochen Xie, Xianfeng Tang, Chen Luo, Yang Li, Joyce C. Ho, Carl Yang, Qi He
| Challenge: | Retrieval-augmented generation (RAG) enhances the question answering abilities of large language models (LLMs) however, adapting general-purpose RAG systems to specialized fields poses unique challenges due to distribution shifts and limited access to domain-specific data. |
| Approach: | They propose a method that equips large language models with joint capabilities of question answering and question generation for domain adaptation. |
| Outcome: | Experiments on 11 datasets across three different domains verify the efficacy of SimRAG over baselines by 1.2%–8.6%. |
RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation (2026.findings-acl)
Copied to clipboard
| Challenge: | Retrieval-augmented generation (RAG) enhances large language models by integrating external knowledge retrieved at inference time. |
| Approach: | They evaluate RAG systems using MassiveDS, a large-scale datastore with mixture of knowledge. |
| Outcome: | The proposed approach improves performance on knowledge-intensive NLP tasks. |
Systematic Knowledge Injection into Large Language Models via Diverse Augmentation for Domain-Specific RAG (2025.findings-naacl)
Copied to clipboard
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. |
| Outcome: | The proposed framework achieves 10% relative gain in token-level recall while preserving the LLM’s generalization capabilities. |
Enhancing Retrieval-Augmented Generation: A Study of Best Practices (2025.coling-main)
Copied to clipboard
| Challenge: | Retrieval-augmented generation systems have shown remarkable advancements by integrating retrieval mechanisms into language models, enhancing their ability to produce more accurate and contextually relevant responses. |
| Approach: | They propose to integrate query expansion, various novel retrieval strategies, and a Contrastive In-Context Learning RAG to improve response quality. |
| Outcome: | The proposed RAGs incorporate query expansion, various novel retrieval strategies, and a novel Contrastive In-Context Learning RAG. |
RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation (2024.emnlp-demo)
Copied to clipboard
Xuanwang Zhang, Yun-Ze Song, Yidong Wang, Shuyun Tang, Xinfeng Li, Zhengran Zeng, Zhen Wu, Wei Ye, Wenyuan Xu, Yue Zhang, Xinyu Dai, Shikun Zhang, Qingsong Wen
| Challenge: | Existing research on Retrieval Augmented Generation (RAG) does not address the problem of hallucinations and real-time updating of knowledge. |
| Approach: | They propose a modular open-source library to equip LLMs with external knowledge. |
| Outcome: | The proposed approach reduces the need for expensive open-source tools and lacks fair comparisons between novel RAG algorithms. |
Unsupervised Information Refinement Training of Large Language Models for Retrieval-Augmented Generation (2024.acl-long)
Copied to clipboard
| Challenge: | Existing studies show that LLMs face challenges in effectively using retrieved information . authors propose a method that considers LLM as "Information Refiner" |
| Approach: | They propose a method that considers LLMs as "Information Refiners" they propose INFO-RAG, which is low-cost and general across various tasks . |
| Outcome: | The proposed method improves performance of LLaMA2 by 9.39% relative points . it is low-cost and general across various tasks, and is robust and in-context learning is possible . |
Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering (2023.tacl-1)
Copied to clipboard
Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, Tharindu Kaluarachchi, Rajib Rana, Suranga Nanayakkara
| Challenge: | Retrieval Augment Generation (RAG) has been developed for use in open-domain question answering (ODQA) but it is not optimized for use with other specialized domains such as healthcare and news. |
| Approach: | They propose an extension to RAG that can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training. |
| Outcome: | The proposed extension can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training. |
RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning (2025.emnlp-main)
Copied to clipboard
Yu Wang, Shiwan Zhao, Zhihu Wang, Ming Fan, Xicheng Zhang, Yubo Zhang, Zhengfan Wang, Heyuan Huang, Ting Liu
| Challenge: | Existing RAG paradigms often overlook the cognitive step of applying knowledge, leaving a gap between retrieved facts and task-specific reasoning. |
| Approach: | They introduce a module extension that integrates application-aware reasoning into the RAG pipeline. |
| Outcome: | Experiments show that RAG+ outperforms standard RAG variants and achieves gains of 3–5% in complex scenarios. |
M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods for retrieving relevant memories from an external database are coarse-grained and can cause noise and focus on crucial memories. |
| Approach: | They propose a multiple partition paradigm for RAG where each database partition serves as a basic unit for execution. |
| Outcome: | The proposed framework outperforms baseline methods on three language generation tasks on seven datasets. |
Domain-Specific Data Generation Framework for RAG Adaptation (2026.findings-acl)
Copied to clipboard
| Challenge: | Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning capabilities of large language models (LLMs) with external retrieval to produce domain-grounded responses. |
| Approach: | They propose a scalable and modular data-centric framework for generating domain-grounded question–answer–context triples tailored to diverse RAG adaptation strategies. |
| Outcome: | The proposed framework generates domain-grounded question–answer–context triples for multiple RAG adaptation strategies. |