4 papers
ArcAligner: Adaptive Recursive Aligner for Compressed Context Embeddings in RAG
Jianbo Li, Yi Jiang, Sendong Zhao +3
Retrieval-Augmented Generation (RAG) helps LLMs stay accurate, but feeding long documents into a prompt makes the model slow and expensive. This has motivated context compression,…
OptiSet: Unified Optimizing Set Selection and Ranking for Retrieval-Augmented Generation
Yi Jiang, Sendong Zhao, Jianbo Li +4
Retrieval-Augmented Generation (RAG) improves generation quality by incorporating evidence retrieved from large external corpora. However, most existing methods rely on statically…
CoCoA: Collaborative Chain-of-Agents for Parametric-Retrieved Knowledge Synergy
Yi Jiang, Sendong Zhao, Jianbo Li +4
Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs), especially for knowledge-intensive tasks. Despite its advantages, current RAG methods often struggle to…
GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis
Yi Jiang, Sendong Zhao, Jianbo Li +2
The Retrieval-Augmented Generation (RAG) framework introduces a retrieval module to dynamically inject retrieved information into the input context of large language models (LLMs),…