Papers with Structured Query Language
Understanding the Effects of Noise in Text-to-SQL: An Examination of the BIRD-Bench Benchmark (2024.acl-short)
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| Challenge: | BIRD-Bench was created to model dirty and noisy database values, but it was not created to contain noise and errors in the questions and gold SQL queries. |
| Approach: | They analysed the distribution and types of noise in the widely used BIRD-Bench benchmark and the impact of noise on models. |
| Outcome: | The BIRD-Bench benchmark contains ambiguous questions and syntactical errors, but it was not designed to contain noise and errors in the questions and gold SQL queries. |
DeTriever: Decoder-representation-based Retriever for Improving NL2SQL In-Context Learning (2025.coling-main)
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Raymond Li, Yuxi Feng, Zhenan Fan, Giuseppe Carenini, Weiwei Zhang, Mohammadreza Pourreza, Yong Zhang
| Challenge: | In-context Learning (ICL) has proven to be effective in a variety of complex tasks, but the selection of the most beneficial demonstration examples remains an open research problem. |
| Approach: | They propose a demonstration retrieval framework that learns a weighted combination of LLM hidden states where rich semantic information is encoded. |
| Outcome: | Experiments on two popular NL2SQL benchmarks show that the proposed method outperforms state-of-the-art models. |
In-Context Reinforcement Learning with Retrieval-Augmented Generation for Text-to-SQL (2025.coling-main)
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| Challenge: | Existing methods of synthetic query generation generate mostly simple queries which might not be sufficiently representative of complex, real world queries. |
| Approach: | They propose to use large language models to fine tune query generation to produce complex queries that practitioners may pose during inference. |
| Outcome: | The proposed framework achieves 15-20% higher recall in database/table retrieval task compared to the existing state-of-the-art models for schema identification and upto 2% higher execution accuracy for SQL generation. |