Summary of Exit: Context-aware Extractive Compression For Enhancing Retrieval-augmented Generation, by Taeho Hwang et al.
EXIT: Context-Aware Extractive Compression for Enhancing Retrieval-Augmented Generation
by Taeho Hwang, Sukmin Cho, Soyeong Jeong, Hoyun Song, SeungYoon Han, Jong C. Park
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We introduce EXIT, an extractive context compression framework that enhances the effectiveness and efficiency of retrieval-augmented generation (RAG) in question answering (QA). RAG systems often struggle when retrieval models fail to rank relevant documents, leading to increased latency and reduced accuracy. EXIT addresses this limitation by classifying sentences from retrieved documents while preserving contextual dependencies, enabling parallelizable, context-aware extraction. Our evaluations on single-hop and multi-hop QA tasks show that EXIT consistently surpasses existing compression methods and even uncompressed baselines in QA accuracy, while reducing inference time and token count. By improving both effectiveness and efficiency, EXIT provides a promising direction for developing scalable, high-quality QA solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We created a new way to make computers better at answering questions by compressing information from the internet. This helps computers give more accurate answers quickly, even when they can’t find all the right information. Before, computers had trouble giving good answers because they had too much extra information that wasn’t important. Our new method, called EXIT, fixes this problem by sorting through the information and only keeping what’s really needed. Tests show that our method works better than other ways of compressing information and gives more accurate answers faster. |
Keywords
» Artificial intelligence » Inference » Question answering » Rag » Retrieval augmented generation » Token
