Arctic Text2SQL: ExCoT
Snowflake’s AI research team introduces ExCoT, the first model in the Arctic Text2SQL family. ExCoT is a novel framework that combines CoT prompting with SQL execution-based DPO, using execution results — not human preferences — as the feedback signal. This enables scalable, high-quality model optimization without requiring expensive human annotations.
Based on our internal testing, ExCoT delivered state-of-the-art results on the BIRD-test benchmark, achieving best-in-class performance in the single-model, single-inference category using only public datasets (BIRD and Spider) and no additional Text2SQL data:
Llama-3.1-Arctic-ExCoT-70B improved execution accuracy on the BIRD-dev set from the base model’s 57.37% to 68.51%. Qwen-2.5-coder-Arctic-ExCoT-32B achieved similarly strong gains.
Both models significantly outperformed other well-known frontier general-purpose models, achieving over 10 points of improvement.
For more details about ExCoT and how to use it:
- ❄️ Arctic Text2SQL: Introducing ExCoT for Execution-Guided Chain-of-Thought Optimization (blog)
- 📝 ExCoT: Optimizing Reasoning for Text-to-SQL with Execution Feedback (arxiv)
- 🚀 Getting started guide using ArcticTraining
Evaluation results
| Model | ||
|---|---|---|
| BIRD Ex% Dev | BIRD Ex% Test | |
| Arctic-ExCoT-70B (LLaMA 3.1 70B) | 68.51 | 68.53 |
| Arctic-ExCoT-32B (Qwen-2.5-Coder 32B) | 68.25 | 68.19 |
| XiYanSQL-QwenCoder* | 67.01 | 69.03 |
| OpenAI GPT-4o | 54.04 | – |
| OpenAI GPT-4 | 46.35 | 54.89 |
| Anthropic Claude 3.5-Sonnet | 50.13 | – |
| Claude-2 | 42.70 | 49.02 |
| OpenAI o1-mini | 52.41 | – |
| OpenAI o3-mini | 53.72 | – |
| Mistral-large-2407 (123B) | 53.52 | 55.84 |
| DeepSeek-V2 (236B) | 56.13 | 56.68 |
Top Single-Model, Single-Inference Results on the BIRD Leaderboard (as of March 25, 2025). *XiYanSQL-QwenCoder: there are some challenges to reproduce the numbers [1][2].
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