Summary of Autoprep: Natural Language Question-aware Data Preparation with a Multi-agent Framework, by Meihao Fan and Ju Fan and Nan Tang and Lei Cao and Guoliang Li and Xiaoyong Du
AutoPrep: Natural Language Question-Aware Data Preparation with a Multi-Agent Framework
by Meihao Fan, Ju Fan, Nan Tang, Lei Cao, Guoliang Li, Xiaoyong Du
First submitted to arxiv on: 10 Dec 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 A machine learning framework, AutoPrep, is proposed for answering natural language questions about tables, enhancing data preparation for accurate responses. This task is crucial as it bridges the gap between human language and machine-readable formats, allowing users to extract meaningful insights from structured data. The approach involves a multi-agent framework that leverages strengths of multiple agents, each specialized in specific data preparation tasks. Key components include planner, programmer, and executor, which perform logical planning, code generation, and execution respectively. Novel mechanisms are designed for high-level operation suggestion and low-level code generation to support the framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AutoPrep is a new way to help computers understand tables and answer questions about them. Right now, it’s hard to get accurate answers because we need to prepare the data first. But with AutoPrep, we can use many different methods to make sure our answers are correct. It’s like having a team of helpers who each do a specific job to get the right answer. |
Keywords
» Artificial intelligence » Machine learning
