The field of Retrieval-Augmented Generation (RAG) is rapidly evolving, with a focus on improving the accuracy and reliability of Large Language Models (LLMs) in various domains. Recent research has explored innovative approaches to enhance RAG, including the development of novel architectures, such as hierarchical retrieval and graph-based retrieval, and the integration of external knowledge sources, like knowledge graphs and databases. These advancements aim to address the limitations of traditional RAG methods, including the issue of hallucination and the need for more efficient and effective retrieval mechanisms. Noteworthy papers in this area include MARAG-R1, which proposes a reinforcement-learned multi-tool RAG framework, and Interact-RAG, which introduces a new paradigm that elevates the LLM agent from a passive query issuer to an active manipulator of the retrieval process. Additionally, papers like DeepSpecs and PROPEX-RAG demonstrate the effectiveness of RAG in specific domains, such as 5G and multi-hop question answering. Overall, the field of RAG is moving towards more sophisticated and specialized approaches, with a focus on improving the performance and reliability of LLMs in a wide range of applications.
Advancements in Retrieval-Augmented Generation
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LLM-Centric RAG with Multi-Granular Indexing and Confidence Constraints
A Memory-Efficient Retrieval Architecture for RAG-Enabled Wearable Medical LLMs-Agents
MARAG-R1: Beyond Single Retriever via Reinforcement-Learned Multi-Tool Agentic Retrieval
Interact-RAG: Reason and Interact with the Corpus, Beyond Black-Box Retrieval
AstuteRAG-FQA: Task-Aware Retrieval-Augmented Generation Framework for Proprietary Data Challenges in Financial Question Answering
IL-PCSR: Legal Corpus for Prior Case and Statute Retrieval
Zero-RAG: Towards Retrieval-Augmented Generation with Zero Redundant Knowledge
Efficient Test-Time Retrieval Augmented Generation
Thought-For-Food: Reasoning Chain Induced Food Visual Question Answering
DeepSpecs: Expert-Level Questions Answering in 5G
PrefixNLI: Detecting Factual Inconsistencies as Soon as They Arise
RAGSmith: A Framework for Finding the Optimal Composition of Retrieval-Augmented Generation Methods Across Datasets
A Graph-based RAG for Energy Efficiency Question Answering
Hybrid Retrieval-Augmented Generation Agent for Trustworthy Legal Question Answering in Judicial Forensics
Towards LLM-Powered Task-Aware Retrieval of Scientific Workflows for Galaxy
PROPEX-RAG: Enhanced GraphRAG using Prompt-Driven Prompt Execution
Tool Zero: Training Tool-Augmented LLMs via Pure RL from Scratch
GRAD: Graph-Retrieved Adaptive Decoding for Hallucination Mitigation
Abductive Inference in Retrieval-Augmented Language Models: Generating and Validating Missing Premises
RAGalyst: Automated Human-Aligned Agentic Evaluation for Domain-Specific RAG
BanglaMedQA and BanglaMMedBench: Evaluating Retrieval-Augmented Generation Strategies for Bangla Biomedical Question Answering