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Welding Natural Language Queries to Analytics IRs with LLMs

Summary: Use LLMs to translate NL analytics queries directly to a custom IR (Weld), making IRs accessible and in some cases improving translation accuracy and end-to-end performance versus SQL-centric nl2sql. nl2weld (GPT-4) uses self-reflection, domain instructions and compiler feedback, attains 77.4% on a Spider subset and emits code 1.2–3x faster than gold SQL/DIN-SQL. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
531
Venue
CIDR
Year
2024
Pagerank
5.093636e-05
Overall Rank
11,120 | 23.71%
DOI
-

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BibTeX Citation

@inproceedings{rajan_cidr24,
        address = {Amsterdam, Netherlands},
        series = {{CIDR} '24},
        title = {{Welding Natural Language Queries to Analytics IRs with LLMs}},
        booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
        author = {Rajan, Kaushik and Rastogi, Aseem and Lal, Akash and Rajendra, Sampath and Subramanian, Krithika and Patel, Krut},
        year = {2024}
}

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