Summary of Mfort-qa: Multi-hop Few-shot Open Rich Table Question Answering, by Che Guan et al.
MFORT-QA: Multi-hop Few-shot Open Rich Table Question Answering
by Che Guan, Mengyu Huang, Peng Zhang
First submitted to arxiv on: 28 Mar 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 proposed Multi-hop Few-shot Open Rich Table QA (MFORT-QA) approach addresses the challenge of summarizing and extracting vital information from large tables. Traditional Table QA tasks may not ensure accurate answers, but advancements in Large Language Models (LLMs) offer new possibilities for tabular data extraction using prompts. The MFORT-QA approach consists of two steps: Few-Shot Learning (FSL), which retrieves relevant tables and contexts based on a given question, and Chain-of-thought (CoT) prompting to decompose complex questions into sequential reasoning thoughts. Retrieval-Augmented Generation (RAG) enhances this process by retrieving relevant table contexts, resulting in more accurate answers from an LLM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new approach called MFORT-QA that helps machines extract important information from big tables. This is useful for professionals who need to summarize many documents quickly. The method uses two steps: first, it finds the right tables and surrounding text based on a question; then, it breaks down complex questions into smaller, more manageable parts. By combining these steps with an LLM like ChatGPT, the approach can provide more accurate answers. |
Keywords
» Artificial intelligence » Few shot » Prompting » Rag » Retrieval augmented generation
