---
title: "CodeS — paper by Li et al. (2024) — summary and ideas"
description: "Most leading text-to-SQL systems rely on closed LLMs such as GPT-4 and ChatGPT. 84 references, cited by 59 papers here."
authors: ["Haoyang Li", "Jing Zhang", "Hanbing Liu", "Ju Fan", "Xiaokang Zhang", "Jun Zhu", "Renjie Wei", "Hongyan Pan", "Cuiping Li", "Hong Chen"]
year: 2024
venue: "arXiv:2402.16347"
source: "https://arxiv.org/abs/2402.16347"
doi: "10.48550/arxiv.2402.16347"
arxiv: "2402.16347"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/li-2024-codes-towards-building-open-source/"
---
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# CodeS: Towards Building Open-source Language Models for Text-to-SQL

**Authors:** [Haoyang Li](https://md.research.elamur.ai/free/en/authors/haoyang-li/), [Jing Zhang](https://md.research.elamur.ai/free/en/authors/jing-zhang/), [Hanbing Liu](https://md.research.elamur.ai/free/en/authors/hanbing-liu/), Ju Fan, [Xiaokang Zhang](https://md.research.elamur.ai/free/en/authors/xiaokang-zhang/), [Jun Zhu](https://md.research.elamur.ai/free/en/authors/jun-zhu/), Renjie Wei, Hongyan Pan, [Cuiping Li](https://md.research.elamur.ai/free/en/authors/cuiping-li/) and [Hong Chen](https://md.research.elamur.ai/free/en/authors/hong-chen/) · **Year:** 2024 · **Venue:** arXiv:2402.16347 · **Publisher:** [Association for Computing Machinery](https://md.research.elamur.ai/free/en/publishers/association-for-computing-machinery/) · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2402.16347) · [PDF](https://arxiv.org/pdf/2402.16347) · [DOI 10.48550/arxiv.2402.16347](https://doi.org/10.48550/arxiv.2402.16347) · [arXiv 2402.16347](https://arxiv.org/abs/2402.16347) · [OpenAlex](https://openalex.org/W4399175046)

**Citations:** 131 citations (OpenAlex, 2026-10-14) · [59](https://md.research.elamur.ai/free/en/papers/li-2024-codes-towards-building-open-source/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Most leading text-to-SQL systems rely on closed LLMs such as GPT-4 and ChatGPT. That brings opaque architectures, privacy concerns from sending data to API providers, and high inference costs. The authors address this with CodeS, a family of fully open-source models with 1B to 15B parameters. CodeS was made by continuing the pre-training of StarCoder on a curated 21.5GB corpus centred on SQL. To help the model match questions to the right tables and columns (schema linking), prompts include only the relevant parts of the schema, column comments, and database values. These values are retrieved in two steps: a BM25 search, then refinement by longest common substring. For new domains with little labelled data, a bi-directional augmentation scheme creates (question, SQL) pairs in two ways: GPT-3.5 expands a few annotated real queries, and benchmark templates are filled with the new database's content. Fine-tuned CodeS matches or beats far larger systems on Spider, BIRD and robustness variants of Spider. It also runs quickly on local hardware, which makes private, low-cost text-to-SQL deployment practical.

## Key points
- **Pre-training corpus (21.5GB):** 11GB of SQL-related data, 6GB of NL-to-code data and 4.5GB of NL-related data. The authors release the corpus together with the code and models.
- **Evaluation scope:**
  - Spider and BIRD; on BIRD's test set the prior leader, DIN-SQL+GPT-4, reaches only about 56%.
  - Spider-DK, Spider-Syn, Spider-Realistic and Dr.Spider, covering 20 test sets in total.
  - Two real-world datasets: Bank-Financials and Aminer-Simplified.
- **Dr.Spider robustness:** SFT CodeS-15B has a global average EX of 75.1, slightly ahead of ChatGPT+ZeroNL2SQL at 74.9 and RESDSQL-3B+NatSQL at 71.7.
- **Domain adaptation (Bank-Financials):** SFT CodeS-7B trained on augmented data reaches 71.4% EX. This compares with 57.1% for 3-shot GPT-3.5 with comments and 26.4% for DIN-SQL+GPT-4.
- **Efficiency:**
  - Inference takes 0.6–1.5 s per Spider sample, versus roughly 60 s for DIN-SQL+GPT-4.
  - In float16, the 1B/3B/7B/15B variants need about 10/13/20/35GB of GPU memory.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Renmin University of China](https://md.research.elamur.ai/free/en/universities/renmin-university-of-china/) — University, Beijing, China · 139 works · 17,752 citations

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## Cite

Cite as: Haoyang Li, Jing Zhang, Hanbing Liu, Ju Fan, Xiaokang Zhang, Jun Zhu, Renjie Wei, Hongyan Pan, Cuiping Li and Hong Chen. 2024. CodeS: Towards Building Open-source Language Models for Text-to-SQL. arXiv:2402.16347.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{li2024codes,
  title = {{CodeS: Towards Building Open-source Language Models for Text-to-SQL}},
  author = {Haoyang Li, Jing Zhang, Hanbing Liu, Ju Fan, Xiaokang Zhang, Jun Zhu, Renjie Wei, Hongyan Pan, Cuiping Li and Hong Chen},
  year = {2024},
  doi = {10.48550/arxiv.2402.16347},
  eprint = {2402.16347},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2402.16347}
}
```

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*Updated 2 November 2026*