Summary of An Adaptive Framework For Generating Systematic Explanatory Answer in Online Q&a Platforms, by Ziyang Chen et al.
An Adaptive Framework for Generating Systematic Explanatory Answer in Online Q&A Platforms
by Ziyang Chen, Xiaobin Wang, Yong Jiang, Jinzhi Liao, Pengjun Xie, Fei Huang, Xiang Zhao
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper proposes SynthRAG, a framework for enhancing Question Answering (QA) systems by handling complex questions that require multi-domain knowledge synthesis. The existing naive RAG models struggle with comprehensive and in-depth answers, leading to logical coherence issues within the generated context. To address this, the authors draw upon systematic thinking theory and introduce dynamic content structuring, generating systematic information for detailed coverage, and producing customized answers tailored to user inquiries. This approach ensures responses are both insightful and methodically organized. Empirical evaluations demonstrate SynthRAG’s superiority in handling complex questions, overcoming naive RAG model limitations, and improving answer quality and depth. Additionally, online deployment on the Zhihu platform reveals notable user engagement, with each response averaging 5.73 upvotes and surpassing human contributors’ performance (79.8%). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps Question Answering systems give better answers by using a new framework called SynthRAG. It’s hard for QA systems to answer complex questions that need information from different areas of knowledge. The authors use an idea called systematic thinking theory to make their framework work. They create outlines that can change and adapt, generating lots of information to cover everything needed. This makes the answers more detailed and logical. Tests show SynthRAG does better than other models in answering complex questions and giving good answers. |
Keywords
» Artificial intelligence » Question answering » Rag
