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FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research
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FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research
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With the advent of large language models (LLMs) and multimodal large language models (MLLMs), the potential of retrieval-augmented generation (RAG) has attracted considerable research attention. Various novel algorithms and models have been introduced to enhance different aspects of RAG systems. However, the absence of a standardized framework for implementation, coupled with the inherently complex RAG process, makes it challenging and time-consuming for researchers to compare and evaluate these approaches in a consistent environment. Existing RAG toolkits, such as LangChain and LlamaIndex, while available, are often heavy and inflexibly, failing to meet the customization needs of researchers. In response to this challenge, we develop \ours{}, an efficient and modular open-source toolkit designed to assist researchers in reproducing and comparing existing RAG methods and developing their own algorithms within a unified framework. Our toolkit has implemented 16 advanced RAG methods and gathered and organized 38 benchmark datasets. It has various features, including a customizable modular framework, multimodal RAG capabilities, a rich collection of pre-implemented RAG works, comprehensive datasets, efficient auxiliary pre-processing scripts, and extensive and standard evaluation metrics. Our toolkit and resources are available at https://github.com/RUC-NLPIR/FlashRAG.
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Cited by 11 Pith papers
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Covering the Unseen: Information Demand Coverage Optimization for Retrieval-Augmented Generation
GeoRAG recasts RAG context selection as monotone submodular Information Demand Coverage Optimization solved via Sinkhorn-Wasserstein distance, delivering +6.5 to +7.5 EM gains over top-k on six QA benchmarks.
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Finding What Matters: Anchoring Context Knowledge with Evolving Indices for Iterative Retrieval
An evolving graph, refreshed at each retrieval step, anchors salient entities and relations and guides iterative retrieval and answer generation in RAG, improving multi-hop QA.
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TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework
TeaRAG shows that hybrid chunk+triplet retrieval with Personalized PageRank and an iterative process-aware DPO reward keeps QA accuracy while cutting reasoning tokens by roughly 60%.
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DCMI: A Differential Calibration Membership Inference Attack Against Retrieval-Augmented Generation
DCMI infers RAG database membership by subtracting the system's yes-probability on a perturbed query from the original query, cancelling the interference of non-member retrieved documents.
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Progressive Multimodal Search and Reasoning for Knowledge-Intensive Visual Question Answering
PMSR progressively constructs structured reasoning trajectories with dual-scope queries and compositional reasoning to improve knowledge acquisition and answer accuracy in knowledge-intensive VQA.
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ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning
ReSearch trains LLMs via RL to integrate search operations into reasoning steps, achieving strong generalization across benchmarks and eliciting reflection and self-correction without supervised reasoning data.
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R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning
R1-Searcher uses two-stage outcome-based RL to train LLMs to invoke external search systems for better reasoning without process rewards or distillation.
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SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
A multi-agent web-search framework that stores progress in shared evidence, coverage, and failure state reports the best F1 scores among compared baselines on WideSearch (80.3 item F1) and GISA (76.5 set F1).
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Personalized Deep Research: A User-Centric Framework, Dataset, and Hybrid Evaluation for Knowledge Discovery
PDR is a user-context-aware framework for LLM research agents that improves report relevance over static baselines, supported by a new dataset and hybrid evaluation.
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RECON: Reasoning with Condensation for Efficient Retrieval-Augmented Generation
Placing a frozen, distilled summarizer between search and reasoning improves RL-RAG exact match (up to 14.5% relative on a 3B agent) while cutting context length by 35%.
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Mitigating Hallucination on Hallucination in RAG via Ensemble Voting
VOTE-RAG applies retrieval voting across diverse queries and response voting across independent generations to mitigate hallucination-on-hallucination in RAG, matching or exceeding complex baselines on six benchmarks ...
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