BIRD-INTERACT: Re-imagining Text-to-SQL Evaluation for Large Language Models via Lens of Dynamic Interactions

📰 ArXiv cs.AI

BIRD-INTERACT reimagines text-to-SQL evaluation for large language models via dynamic interactions

advanced Published 25 Mar 2026

Action Steps

Re-evaluate existing text-to-SQL benchmarks to account for dynamic interactions
Develop new evaluation metrics that consider conversation history and user requirements
Implement BIRD-INTERACT to assess the performance of large language models in multi-turn interactions

Who Needs to Know This

Data scientists and AI engineers working on natural language processing and database applications can benefit from this research as it provides a more realistic evaluation framework for text-to-SQL tasks

Key Insight

💡 Existing multi-turn benchmarks are insufficient for evaluating large language models in real-world database applications

Key Takeaways

BIRD-INTERACT reimagines text-to-SQL evaluation for large language models via dynamic interactions

Full Article

Title: BIRD-INTERACT: Re-imagining Text-to-SQL Evaluation for Large Language Models via Lens of Dynamic Interactions

Abstract:
arXiv:2510.05318v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated remarkable performance on single-turn text-to-SQL tasks, but real-world database applications predominantly require multi-turn interactions to handle ambiguous queries, execution errors, and evolving user requirements. Existing multi-turn benchmarks fall short by treating conversation histories as static context or limiting evaluation to read-only operations, failing to reflect production-grade dat

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