Summary of Interactive-t2s: Multi-turn Interactions For Text-to-sql with Large Language Models, by Guanming Xiong et al.
Interactive-T2S: Multi-Turn Interactions for Text-to-SQL with Large Language Models
by Guanming Xiong, Junwei Bao, Hongfei Jiang, Yang Song, Wen Zhao
First submitted to arxiv on: 9 Aug 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 This study leverages large language models (LLMs) to explore text-to-SQL parsing, addressing scalability issues in processing wide tables. Existing methods lack a step-by-step, interpretable SQL generation process or efficient interaction design. The proposed Interactive-T2S framework generates SQL queries through direct interactions with databases, featuring four tools for proactive and efficient information retrieval by the LLM. Exemplars demonstrate the step-wise reasoning processes within the framework. Our experiments on the BIRD-Dev dataset reveal state-of-the-art results with only two exemplars, showcasing the effectiveness and robustness of Interactive-T2S. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses big language models to help computers understand text and turn it into SQL code. Right now, this process is slow when dealing with lots of data. Some methods don’t show you how they make their decisions or are hard to use. The researchers created a new way called Interactive-T2S that lets the computer talk directly to the database to find what it needs. They also came up with special tools to help the computer understand what’s being asked and make smart choices. When they tested this method on some data, it worked really well and was better than other methods. |
Keywords
» Artificial intelligence » Parsing
