Summary of Open-sql Framework: Enhancing Text-to-sql on Open-source Large Language Models, by Xiaojun Chen and Tianle Wang and Tianhao Qiu and Jianbin Qin and Min Yang
Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models
by Xiaojun Chen, Tianle Wang, Tianhao Qiu, Jianbin Qin, Min Yang
First submitted to arxiv on: 4 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Despite the success of large language models (LLMs) in Text-to-SQL tasks, open-source LLMs face challenges in contextual understanding and response coherence. Our paper presents a systematic methodology, ours, tailored for Text-to-SQL with open-source LLMs. We evaluate open-source LLMs, introduce the openprompt strategy for effective question representation, novel strategies for supervised fine-tuning, Chain-of-Thought inference, and enhanced few-shot learning through openexample. Token-efficient techniques address challenges in large-scale databases. Our findings highlight the need to investigate the impact of supervised fine-tuning on contextual learning capabilities. Notably, our method significantly improves Llama2-7B from 2.54% to 41.04% and Code Llama-7B from 14.54% to 48.24% on the BIRD-Dev dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models better at understanding natural language and answering questions correctly. Right now, even the best models have trouble understanding the context of a question and giving a good answer. We came up with new ways to help these models learn by looking at how they respond step-by-step. This helps them understand the question better and give more accurate answers. We also found some clever tricks to make the models work faster and more efficiently, which is important when dealing with big databases. The results show that our methods can improve language model performance significantly. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Inference » Language model » Llama » Supervised » Token
