Summary of Benchmarking the Text-to-sql Capability Of Large Language Models: a Comprehensive Evaluation, by Bin Zhang et al.
Benchmarking the Text-to-SQL Capability of Large Language Models: A Comprehensive Evaluation
by Bin Zhang, Yuxiao Ye, Guoqing Du, Xiaoru Hu, Zhishuai Li, Sun Yang, Chi Harold Liu, Rui Zhao, Ziyue Li, Hangyu Mao
First submitted to arxiv on: 5 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 Large Language Models (LLMs) have revolutionized the Text-to-SQL task, outperforming traditional methods. However, there is no consensus on optimal prompt templates and design frameworks. Existing benchmarks inadequately explore LLMs’ performance across sub-tasks, hindering assessment of cognitive capabilities and optimization of LLM-based solutions. To address this, we construct a new dataset to mitigate overfitting risk in LLMs and formulate five evaluation tasks to comprehensively assess diverse methods across various LLMs. Our study highlights performance disparities among LLMs and proposes optimal in-context learning solutions tailored to each task. This research offers valuable insights for enhancing the development of LLM-based Text-to-SQL systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers have been working on a project that helps computers understand natural language and turn it into SQL code. They’ve made some big improvements, but there’s still no agreement on how to make it work better. The current way of testing these computer models is also limited, which makes it hard to know what they can really do. To fix this, we created a new set of examples for the computers to practice with and five different ways to test their abilities. Our study shows that different models have different strengths and weaknesses, and we give suggestions on how to make them work better. This research helps us create more useful computer systems. |
Keywords
» Artificial intelligence » Optimization » Overfitting » Prompt
