Pith. sign in

REVIEW 4 cited by

A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2303.13547 v1 pith:67M3X7J7 submitted 2023-03-12 cs.CL cs.AI

Aiwei Liu , Xuming Hu , Lijie Wen , Philip S. Yu This is my paper · ORCID
classification cs.CLcs.AI
keywords chatgpttext-to-sqlmodelperformancezero-shotabilitiescomprehensiveconducted
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This paper presents the first comprehensive analysis of ChatGPT's Text-to-SQL ability. Given the recent emergence of large-scale conversational language model ChatGPT and its impressive capabilities in both conversational abilities and code generation, we sought to evaluate its Text-to-SQL performance. We conducted experiments on 12 benchmark datasets with different languages, settings, or scenarios, and the results demonstrate that ChatGPT has strong text-to-SQL abilities. Although there is still a gap from the current state-of-the-art (SOTA) model performance, considering that the experiment was conducted in a zero-shot scenario, ChatGPT's performance is still impressive. Notably, in the ADVETA (RPL) scenario, the zero-shot ChatGPT even outperforms the SOTA model that requires fine-tuning on the Spider dataset by 4.1\%, demonstrating its potential for use in practical applications. To support further research in related fields, we have made the data generated by ChatGPT publicly available at https://github.com/THU-BPM/chatgpt-sql.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ErrorLLM: Modeling SQL Errors for Text-to-SQL Refinement

    cs.CL 2026-03 conditional novelty 6.0 of 10

    A fine-tuned model with special error tokens detects SQL mistakes and guides LLMs to fix them, raising execution accuracy on BIRD from 55.87% to 66.23% and on Spider from 75.44% to 86.94% over GPT-4o.

  2. Interactive Text-to-SQL via Expected Information Gain for Disambiguation

    cs.DB 2025-07 reject novelty 4.0 of 10

    An interactive text-to-SQL framework selects clarification questions by expected information gain over a distribution of candidate SQL queries.

  3. Exploring the Landscape of Text-to-SQL with Large Language Models: Progresses, Challenges and Opportunities

    cs.CL 2025-05 conditional novelty 4.0 of 10

    A systematic review organizing LLM-based text-to-SQL methods into pre-processing, in-context learning, fine-tuning, and post-processing paradigms, with a catalog of datasets, metrics, challenges, and future directions.

  4. Chatting with your ERP: A Recipe

    cs.AI 2025-07 conditional novelty 3.0 of 10

    A dual-agent LLM system with a reasoner and critic achieves up to 10/11 expert-validated SQL accuracy on a real ERP database, but the evaluation lacks a baseline and released artifacts.

Pith tools