Pith. sign in

REVIEW 3 cited by

Is GPT-4 a Good Data Analyst?

Not yet reviewed by Pith; the record is open.

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 2305.15038 v2 pith:STLZZINN submitted 2023-05-24 cs.CL

classification cs.CL
keywords datagpt-4analystanalystsconclusiondomainsgenerationgood
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

As large language models (LLMs) have demonstrated their powerful capabilities in plenty of domains and tasks, including context understanding, code generation, language generation, data storytelling, etc., many data analysts may raise concerns if their jobs will be replaced by artificial intelligence (AI). This controversial topic has drawn great attention in public. However, we are still at a stage of divergent opinions without any definitive conclusion. Motivated by this, we raise the research question of "is GPT-4 a good data analyst?" in this work and aim to answer it by conducting head-to-head comparative studies. In detail, we regard GPT-4 as a data analyst to perform end-to-end data analysis with databases from a wide range of domains. We propose a framework to tackle the problems by carefully designing the prompts for GPT-4 to conduct experiments. We also design several task-specific evaluation metrics to systematically compare the performance between several professional human data analysts and GPT-4. Experimental results show that GPT-4 can achieve comparable performance to humans. We also provide in-depth discussions about our results to shed light on further studies before reaching the conclusion that GPT-4 can replace data analysts.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

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

  1. PlotGen: Multi-Agent LLM-based Scientific Data Visualization via Multimodal Feedback

    cs.CL 2025-02 conditional novelty 4.0 of 10

    PlotGen, a five-agent LLM system, improves automated scientific chart generation by adding numeric, lexical, and visual feedback to iteratively fix plotting errors.

  2. A Survey on Large Language Models with some Insights on their Capabilities and Limitations

    cs.CL 2025-01 unverdicted novelty 3.0 of 10

    A broad survey of LLM methods and applications, plus an empirical section on how code-rich pretraining may influence chain-of-thought reasoning, the details of which are not visible in the supplied text.

  3. Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions

    eess.SY 2025-01 conditional novelty 2.0 of 10

    The paper surveys recent work, models, applications, and challenges of using LLMs in intelligent transportation systems, without presenting new experimental results.

Pith tools