Summary of Evaluating and Enhancing Llms For Multi-turn Text-to-sql with Multiple Question Types, by Ziming Guo et al.
Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types
by Ziming Guo, Chao Ma, Yinggang Sun, Tiancheng Zhao, Guangyao Wang, Hai Huang
First submitted to arxiv on: 21 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 The proposed MMSQL test suite for large language models (LLMs) aims to comprehensively evaluate their capabilities in text-to-SQL systems, including question classification and SQL generation. By simulating real-world scenarios with diverse question types and multi-turn Q&A interactions, MMSQL aims to bridge the gap between current LLM-based methods and the complexities of conversational queries. The study assesses the performance of popular LLMs and identifies key factors impacting their performance in such scenarios. Furthermore, an LLM-based multi-agent framework is introduced, which employs specialized agents to identify question types and determine appropriate answering strategies. Experimental results demonstrate that this approach enhances the model’s ability to navigate conversational dynamics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have made great progress in text-to-SQL systems, but they often focus too much on SQL generation and forget about real-world conversations. This can lead to unreliable answers for tricky questions that don’t fit into simple SQL queries. To fix this problem, scientists created a special test suite called MMSQL. It helps evaluate language models’ ability to understand and answer complex questions in a conversational way. The study tested many popular language models and found what makes them good or bad at handling real-world conversations. |
Keywords
» Artificial intelligence » Classification
