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--- |
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license: cc-by-nc-4.0 |
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base_model: |
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- Qwen/Qwen2.5-Coder-32B |
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--- |
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# ExCoT |
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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. |
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Based on our internal testing, ExCoT delivered state-of-the-art results on the [BIRD-test benchmark](https://bird-bench.github.io/), achieving best-in-class performance in the single-model, single-inference category using only public datasets (BIRD and Spider) and no additional Text2SQL data: |
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* Arctic-ExCoT-70B (based on LLaMA 3.1 70B) improved execution accuracy on the BIRD-dev set from the base model’s 57.37% to 68.51%. Arctic-ExCoT-32B (based on Qwen-2.5-Coder 32B) achieved similarly strong gains. |
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* Both models significantly outperformed larger open-weight models, such as Mistral 123B, and even proprietary systems including GPT-4o and Claude 3.5 — achieving over 12 percentage points of improvement. |
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For more details about ExCoT and how to use it: |
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* ❄️ [Arctic Text2SQL: Introducing ExCoT for Execution-Guided Chain-of-Thought Optimization (blog)]() |
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* 📝 [ExCoT: Optimizing Reasoning for Text-to-SQL with Execution Feedback (arxiv)](https://arxiv.org/pdf/2503.19988) |
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* 🚀 [Getting started guide using ArcticTraining](https://github.com/snowflakedb/ArcticTraining/tree/main/projects/excot_dpo) |
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