ELITE: Embedding-Less retrieval with Iterative Text Exploration
This addresses retrieval inefficiencies in RAG for document-level tasks, offering a more efficient alternative to embedding-based methods, though it appears incremental as it builds on existing RAG concepts.
The paper tackles the problem of embedding-based retrieval in RAG systems often retrieving semantically similar but intent-misaligned content, and proposes an embedding-free framework using LLM-based iterative refinement, achieving improved performance on long-context QA benchmarks while reducing storage and runtime by over an order of magnitude.
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.