HLR-SQL: Human-like reasoning for Text-to-SQL with the human in the loop

Recent LLM-based approaches have achieved impressive results on Text-to-SQL benchmarks such as Spider and Bird. However, these benchmarks do not accurately reflect the complexity typically encountered in real-world enterprise scenarios, where queries often span multiple tables. In this paper, we introduce HLR-SQL, a new approach designed to handle such complex enterprise SQL queries. Unlike existing methods, HLR-SQL imitates H uman- L ike R easoning with LLMs by incrementally composing queries through a sequence of intermediate steps, gradually building up to the full query. This is an extended version of Eckmann et al. (2025). The new contributions are centered around incorporating human feedback directly into the reasoning process of HLR-SQL. We evaluate HLR-SQL on a newly constructed benchmark, Spider-HJ, which systematically increases query complexity by splitting tables in the original Spider dataset to raise the average join count needed by queries. Our experiments show that state-of-the-art models experience up to a 70% drop in execution accuracy on Spider-HJ, while HLR-SQL achieves a 9.51% improvement over the best existing approaches on the Spider leaderboard. Finally, we extended HLR-SQL to incorporate human feedback directly into the reasoning process by allowing the LLM to selectively ask for human help when faced with ambiguity or execution errors. We demonstrate that including the human in the loop in this way yields significantly higher accuracy, particularly for complex queries.