EffComp is a new framework for prompt compression in retrieval‑augmented question answering. It uses BERT‑style document rerankers and sentence selectors to perform fast, extractive compression at the sentence level, thereby avoiding hallucinations. The hybrid reinforcement‑plus‑supervised training optimizes compression ratio while maintaining LLM accuracy, and experiments show EffComp outperforms existing methods with low latency.
By Romtham Tungsununtham, Piyawat Lertvittayakumjorn, Atiwong Suchato, Peerapon Vateekul
arXiv:2608. 13588v1 Announce Type: cross Abstract: Multi-hop question answering requires complex reasoning across multiple evidence segments, which often overwhelms retrieval-augmented generation systems with lengthy and noisy contexts, thereby undermining both efficiency and accuracy.
By JungMin Yun, YoungBin Kim
SCOPE is a training‑free generative prompt‑compression framework that reduces LLM input length by chunking a prompt into semantically coherent segments, rewriting each chunk to be more concise, and then reconstructing a coherent prompt. Unlike token‑removal methods, SCOPE’s chunk‑level rewriting preserves critical information and text coherence, and includes optimization techniques for finer‑grained control of compression ratios. Extensive evaluations on question‑answering and summarization tasks show that SCOPE consistently outperforms selective compression baselines, especially at high compression ratios.
By Tinghui Zhang, Yifan Wang, Daisy Zhe Wang
The paper introduces DEX-Comp, a two‑stage training method for soft context compression in Retrieval‑Augmented Generation (RAG). First, a pure distillation warm‑start trains the compression model on correct responses from an uncompressed RAG. Then, hard exploration uses reinforcement learning on queries where the uncompressed RAG fails, encouraging better computation patterns for compressed representations. Experiments on five open‑domain QA benchmarks show that DEX‑Comp compresses retrieved contexts 16×, speeds inference 4×–24×, and matches or surpasses the uncompressed RAG baseline across various retrieval depths.
By Shuyu Guo, Shuo Zhang, Zhaochun Ren
arXiv:2505. 23277v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) often suffers from long and noisy retrieved contexts.
By Yong Zhang, Heng Li, Yanwen Huang, Ning Cheng, Yang Guo, Yun Zhu, Yanmeng Wang, Shaojun Wang, Jing Xiao
arXiv:2603.09222v2 Announce Type: replace
Abstract: Efficient context compression is critical for retrieval-augmented question answering in resource-constrained settings, where long retrieved context...
By Thao Do, Dinh Phu Tran, An Vo, Seon Kwon Kim, Daeyoung Kim