Summary of Grounding Language Model with Chunking-free In-context Retrieval, by Hongjin Qian et al.
Grounding Language Model with Chunking-Free In-Context Retrieval
by Hongjin Qian, Zheng Liu, Kelong Mao, Yujia Zhou, Zhicheng Dou
First submitted to arxiv on: 15 Feb 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 This paper introduces a novel Chunking-Free In-Context (CFIC) retrieval approach for Retrieval-Augmented Generation (RAG) systems, overcoming traditional RAG system limitations. CFIC addresses challenges like processing lengthy documents and filtering out irrelevant content, enabling precise grounding of responses using evidence text. The proposed method bypasses document chunking, a common solution that disrupts semantic coherence or fails to address noise and inaccuracy in evidence retrieval. This work has implications for improving the overall performance and effectiveness of RAG systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for computers to understand long pieces of text by creating a new way to look for specific words or phrases within those texts. Right now, computers can struggle with this task because they have trouble sorting through lots of irrelevant information and finding what they’re actually looking for. This new approach helps computers do this job better by avoiding a common technique that can make the text confusing or hard to understand. |
Keywords
» Artificial intelligence » Grounding » Rag » Retrieval augmented generation
