Papers › ELITE: Embedding-Less retrieval with Iterative Text Exploration

ELITE: Embedding-Less retrieval with Iterative Text Exploration

17 May 2025arXiv:2505.11908archive 2025-07-28

Zhangyu Wang, Siyuan Gao, Rong Zhou, Hao Wang, Li Ning

Large Language Models (LLMs) have achieved impressive progress in natural language processing, but their limited ability to retain long-term context constrains performance on document-level or multi-turn tasks. Retrieval-Augmented Generation (RAG) mitigates this by retrieving relevant information from an external corpus. However, existing RAG systems often rely on embedding-based retrieval trained on corpus-level semantic similarity, which can lead to retrieving content that is semantically similar in form but misaligned with the question's true intent. Furthermore, recent RAG variants construct graph- or hierarchy-based structures to improve retrieval accuracy, resulting in significant computation and storage overhead. In this paper, we propose an embedding-free retrieval framework. Our method leverages the logical inferencing ability of LLMs in retrieval using iterative search space refinement guided by our novel importance measure and extend our retrieval results with logically related information without explicit graph construction. Experiments on long-context QA benchmarks, including NovelQA and Marathon, show that our approach outperforms strong baselines while reducing storage and runtime by over an order of magnitude.

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tjzvbokbnft/ELITE-Embedding-Less-retrieval-with-Iterative-Text-Exploration officialmentioned in papermentioned on GitHub report

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Tasks

RAG Retrieval Retrieval-augmented Generation Semantic Similarity Semantic Textual Similarity graph construction

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Methods

Adam Attention Attention Dropout BART BERT BPE Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention RAG Residual Connection Softmax Weight Decay WordPiece

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