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        <title>Quality Assessment of Tabular Data using Large Language Models and Code Generation</title>
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    <name type="personal">
        <namePart type="given">Ashlesha</namePart>
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        <namePart type="given">Akshar</namePart>
        <namePart type="family">Kaul</namePart>
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    <name type="personal">
        <namePart type="given">Krishnasuri</namePart>
        <namePart type="family">Narayanam</namePart>
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    <name type="personal">
        <namePart type="given">Sameep</namePart>
        <namePart type="family">Mehta</namePart>
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            <title>Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track</title>
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            <namePart type="given">Saloni</namePart>
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    <abstract>Reliable data quality is crucial for downstream analysis of tabular datasets, yet rule-based validation often struggles with inefficiency, human intervention, and high computational costs. We present a three-stage framework that combines statistical inliner detection with LLM-driven rule and code generation. After filtering data samples through traditional clustering, we iteratively prompt LLMs to produce semantically valid quality rules and synthesize their executable validators through code-generating LLMs. To generate reliable quality rules, we aid LLMs with retrieval-augmented generation (RAG) by leveraging external knowledge sources and domain-specific few-shot examples. Robust guardrails ensure the accuracy and consistency of both rules and code snippets. Extensive evaluations on benchmark datasets confirm the effectiveness of our approach.</abstract>
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%0 Conference Proceedings
%T Quality Assessment of Tabular Data using Large Language Models and Code Generation
%A Akella, Ashlesha
%A Kaul, Akshar
%A Narayanam, Krishnasuri
%A Mehta, Sameep
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F akella-etal-2025-quality
%X Reliable data quality is crucial for downstream analysis of tabular datasets, yet rule-based validation often struggles with inefficiency, human intervention, and high computational costs. We present a three-stage framework that combines statistical inliner detection with LLM-driven rule and code generation. After filtering data samples through traditional clustering, we iteratively prompt LLMs to produce semantically valid quality rules and synthesize their executable validators through code-generating LLMs. To generate reliable quality rules, we aid LLMs with retrieval-augmented generation (RAG) by leveraging external knowledge sources and domain-specific few-shot examples. Robust guardrails ensure the accuracy and consistency of both rules and code snippets. Extensive evaluations on benchmark datasets confirm the effectiveness of our approach.
%U https://aclanthology.org/2025.emnlp-industry.183/
%P 2713-2748

Markdown (Informal)

[Quality Assessment of Tabular Data using Large Language Models and Code Generation](https://aclanthology.org/2025.emnlp-industry.183/) (Akella et al., EMNLP 2025)

• Quality Assessment of Tabular Data using Large Language Models and Code Generation (Akella et al., EMNLP 2025)

ACL

• Ashlesha Akella, Akshar Kaul, Krishnasuri Narayanam, and Sameep Mehta. 2025. Quality Assessment of Tabular Data using Large Language Models and Code Generation. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 2713–2748, Suzhou (China). Association for Computational Linguistics.