arXiv AI
KSE-Web: An Analysis of Hybrid Retrieval and LLM-Assisted Query Expansion for Low-Resource Khmer Semantic Search
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DEPT: Document Embedding Preservation Tuning for Unified Query Expansion and Retrieval
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STAIR (STructure Aware Information Retriever): A novel dataset and LLM based retriever for document structure augmentation
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arXiv AI
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Multimodal Hybrid Retrieval-Augmented Generation for Scientific Document Understanding using Open-Source SLMs
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M-RAG: Semantic Key-Value Indexing for Retrieval-Augmented Generation
arXiv:2603. 26667v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) turns external documents into evidence for large language models.
arXiv AI
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Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrieval
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arXiv AI
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Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling
The paper introduces LLM-QL, a dense retrieval model that harnesses large language models (LLMs) by maximizing query likelihood (QL) as an auxiliary task. It incorporates an Attention Block to limit predictive token attention to document tokens before the ending token and a Document Corruption component that masks parts of the document during prediction. Experiments on MS MARCO and BEIR datasets show that LLM-QL outperforms other LLM-based retrievers, and detailed analyses confirm the effectiveness of its components.