LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation
Ranking
Overall
78
Content
95
Popularity
37
Observed public metrics from 1 member.
Merged summary
TL;DR - LiteRAG is a graph-based RAG method that replaces retrieval-time LLM control with query-conditioned graph exploration and compact reasoning-chain construction. It maintains or improves retrieval quality while substantially reducing latency, cost, and context size.
- Achieves the highest reported overall quality on DistComp (0.798), a multi-hop retrieval benchmark for distributed-systems papers.
- Reduces per-query latency by over 100Ă— and cost by over 99% versus GraphRAG Global and DRIFT.
- Matches LinearRAG’s overall quality on UltraDomain while using roughly 14× fewer tokens.
- Ablations identify query-adaptive thresholding and community-aware hub penalization as the main contributors to token efficiency.
Sources (1)
LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation
Public signals
Semantic Scholar citations 0 · Semantic Scholar influential citations 0
TL;DR - LiteRAG is a graph-based RAG method that replaces retrieval-time LLM control with query-conditioned graph exploration and compact reasoning-chain construction. It maintains or improves retrieval quality while substantially reducing latency, cost, and context size.
- Achieves the highest reported overall quality on DistComp (0.798), a multi-hop retrieval benchmark for distributed-systems papers.
- Reduces per-query latency by over 100Ă— and cost by over 99% versus GraphRAG Global and DRIFT.
- Matches LinearRAG’s overall quality on UltraDomain while using roughly 14× fewer tokens.
- Ablations identify query-adaptive thresholding and community-aware hub penalization as the main contributors to token efficiency.