Open access
2026
SAC-RAG: Semantic Adaptive Context Compression for Retrieval-Augmented Generation
Experimental results show that SAC-RAG reduces token consumption by 38%–58% at the cost of only a 1–2 percentage point EM drop, with EM actually improving after compression for reasoning-type questions, achieving the optimal quality–efficiency trade-off in terms of token consumption.
Deyu Zhang, Hongqiang Yu, Jinze Huo et al.
· IEEE Access · 0 citations