Pair Programming with ChatGPT for Sampling and Estimation of Copulas

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19520%2F23%3AA0000366" target="_blank" >RIV/47813059:19520/23:A0000366 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s00180-023-01437-2" target="_blank" >https://link.springer.com/article/10.1007/s00180-023-01437-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00180-023-01437-2" target="_blank" >10.1007/s00180-023-01437-2</a>

Result language

angličtina

Original language name

Pair Programming with ChatGPT for Sampling and Estimation of Copulas

Original language description

Without writing a single line of code by a human, an example Monte Carlo simulation-based application for stochastic dependence modeling with copulas is developed through pair programming involving a human partner and a large language model (LLM) fine-tuned for conversations. This process encompasses interacting with ChatGPT using both natural language and mathematical formalism. Under the careful supervision of a human expert, this interaction facilitated the creation of functioning code in MATLAB, Python, and R. The code performs a variety of tasks including sampling from a given copula model, evaluating the model’s density, conducting maximum likelihood estimation, optimizing for parallel computing on CPUs and GPUs, and visualizing the computed results. In contrast to other emerging studies that assess the accuracy of LLMs like ChatGPT on tasks from a selected area, this work rather investigates ways how to achieve a successful solution of a standard statistical task in a collaboration of a human expert and artificial intelligence (AI). Particularly, through careful prompt engineering, we separate successful solutions generated by ChatGPT from unsuccessful ones, resulting in a comprehensive list of related pros and cons. It is demonstrated that if the typical pitfalls are avoided, we can substantially benefit from collaborating with an AI partner. For example, we show that if ChatGPT is not able to provide a correct solution due to a lack of or incorrect knowledge, the human-expert can feed it with the correct knowledge, e.g., in the form of mathematical theorems and formulas, and make it to apply the gained knowledge in order to provide a correct solution. Such ability presents an attractive opportunity to achieve a programmed solution even for users with rather limited knowledge of programming techniques.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA21-03085S" target="_blank" >GA21-03085S: Supporting Decision Processes with Pairwise Comparisons and Data Mining</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computational Statistics

ISSN

0943-4062

e-ISSN

1613-9658

Volume of the periodical

Neuveden

Issue of the periodical within the volume

Neuveden

Country of publishing house

CH - SWITZERLAND

Number of pages

31

Pages from-to

1-31

UT code for WoS article

001110962300001

EID of the result in the Scopus database

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