ImpRAG: Retrieval-Augmented Generation with Implicit Queries (2025.findings-emnlp)
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| Challenge: | Retrieval-Augmented Generation (RAG) systems treat retrieval and generation as separate processes, requiring explicit textual queries to connect them. |
| Approach: | They propose a query-free RAG system that integrates retrieval and generation into a unified model. |
| Outcome: | The proposed system can achieve 3.6-11.5 accuracy improvements on unseen tasks . it allows models to express their information needs without human-specified queries . |
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UniRAG: Unified Query Understanding Method for Retrieval Augmented Generation (2025.acl-long)
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| Challenge: | Existing query augmentation methods face knowledge update lag and hallucinations in large language models (LLMs) Existing methods face two key challenges: (1) separation of query augmented and encoding tasks, which hinders information sharing and introduces cumulative errors; (2) difficulty of selecting optimal augmentation strategy for different scenarios. |
| Approach: | They propose a unified framework for query understanding in RAG that integrates internal and external knowledge to enhance query augmentation and encoding tasks. |
| Outcome: | The proposed framework outperforms traditional query augmentation methods in five knowledge-intensive benchmark tasks in both closed and open domain question answering. |
Enhancing Retrieval-Augmented Generation: A Study of Best Practices (2025.coling-main)
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| 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. |
FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation (2025.acl-demo)
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| Challenge: | Existing frameworks for retrieval-augmented generation (RAG) lack new techniques, difficulties in algorithm reproduction and sharing, and high system overhead. |
| Approach: | They propose a retrieval-augmented generation framework specifically designed for research and prototyping that supports text-based, multimodal, and network-based RAG. |
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Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)
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Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Retrieval-augmented generation (RAG) techniques have proven to be effective in integrating up-to-date information, mitigating hallucinations, and enhancing response quality, especially in specialized domains. |
| Approach: | They propose several strategies for deploying RAG that balance performance and efficiency. |
| Outcome: | The proposed approaches can significantly enhance question-answering capabilities and accelerate the generation of multimodal content using a “retrieval as generation” strategy. |
Retrieval-augmented Generation across Heterogeneous Knowledge (2022.naacl-srw)
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| Challenge: | Existing methods for retrieving knowledge from a single source homogeneous corpus have been gaining increasing attention in the field of natural language processing (NLP) however, they still suffer from the following drawbacks: (i) They are usually trained offline, making the model agnostic to the latest information, e.g., asking a chat-bot about COVID-19. |
| Approach: | They propose to use a single-source homogeneous corpus to generate retrieval-augmented generation models that can learn from the pre-training corpus. |
| Outcome: | The proposed methods have been applied to various knowledge-intensive NLP tasks, but most of the work has focused on retrieving unstructured text documents from Wikipedia. |
SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains (2025.naacl-long)
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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%. |
TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning (2025.emnlp-main)
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| Challenge: | Existing approaches to retrievalaugmented generation (RAG) are limited when applied to heterogeneous documents . flattening tables and chunking strategies disrupt tabular structure, leads to information loss, and undermines reasoning capabilities of LLMs in multi-hop, global queries. |
| Approach: | They propose a SQL-based framework that unifies textual understanding and complex manipulations over tabular data. |
| Outcome: | The proposed framework outperforms baselines on public datasets and HeteQA on heterogeneous document question answering. |
RichRAG: Crafting Rich Responses for Multi-faceted Queries in Retrieval-Augmented Generation (2025.coling-main)
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| Challenge: | Existing studies focus on question scenarios with clear user intents and concise answers, but it is prevalent that users issue broad, open-ended queries with diverse sub-intents. |
| Approach: | They propose a framework that includes a sub-aspect explorer and a multi-faceted retriever to build a candidate pool of diverse external documents related to these sub-intents. |
| Outcome: | The proposed framework provides comprehensive and satisfying responses to users on two publicly available datasets. |
Accelerating Adaptive Retrieval Augmented Generation via Instruction-Driven Representation Reduction of Retrieval Overlaps (2025.findings-acl)
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| Challenge: | Existing approaches to augmented generation ignore the overlap in retrieval results . overlapping content is redundantly represented, affecting the overall efficiency. |
| Approach: | They propose a model-agnostic approach to re-augmented generation that speeds up prefilling and decoding . they propose an instruction-driven module to guide the model to more suitable ways for LLMs . |
| Outcome: | The proposed approach achieves 2.79 and 2.33 times significant acceleration on average for prefilling and decoding respectively while maintaining equal generation quality. |
Query Decomposition for RAG: Balancing Exploration-Exploitation (2026.eacl-long)
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Roxana Petcu, Kenton Murray, Daniel Khashabi, Evangelos Kanoulas, Maarten de Rijke, Dawn Lawrie, Kevin Duh
| Challenge: | Complex user queries often involve the exclusion of information, negation, or missing entities. |
| Approach: | They propose to decompose user requests into subqueries, retrieve potentially relevant documents for each and then aggregate them to generate an answer. |
| Outcome: | The proposed method achieves 35% gain in document-level precision and 15% increase in -nDCG . it also improves the downstream task of long-form generation. |