LiteSemRAG: Lightweight LLM-Free Semantic-Aware Graph Retrieval for Robust RAG
Graph-based Retrieval-Augmented Generation (RAG) has shown great potential for improving multi-level reasoning and structured evidence aggregation. However, existing graph-based RAG frameworks heavily rely on exploiting large language models (LLMs) during indexing and querying, leading to high token consumption, computational cost and latency overhead. In this paper, we propose LiteSemRAG, a lightweight, fully LLM-free, semantic-aware graph retrieval framework. LiteSemRAG constructs a heterogeneous semantic graph by exploiting contextual token-level embeddings, explicitly separating surface lexical representations from context-dependent semantic meanings. To robustly model polysemy, we in...
| Authors | Xiao Yue, Guangzhi Qu, Lige Gan |
|---|---|
| Primary category | cs.IR |
| Published | 16 March 2026 |
| Last updated at source | 16 March 2026 |
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