Optimizing Small Language Models for NL2SQL via Chain-of-Thought Fine-Tuning

1. 1Identify the limitations of large language models for NL2SQL tasks, including high inference costs
2. 2Explore the efficacy of fine-tuning both large and small language models on NL2SQL tasks
3. 3Apply chain-of-thought fine-tuning to small language models to optimize performance
4. 4Evaluate the results and compare the performance of fine-tuned small language models with large language models

Natural Language Processing (NLP) engineers and data scientists on a team can benefit from this research as it provides a cost-effective solution for NL2SQL tasks, allowing for more efficient data democratization in enterprises

💡 Chain-of-thought fine-tuning can be an effective approach to optimize small language models for NL2SQL tasks, reducing the need for large and costly models

🚀 Fine-tune small language models for NL2SQL with chain-of-thought and reduce inference costs!