Pair Programming with Large Language Models for Sampling and Estimation of Copulas

• URL: http://arxiv.org/abs/2303.18116v1
• Date: Fri, 31 Mar 2023 15:02:48 GMT
• Title: Pair Programming with Large Language Models for Sampling and Estimation of Copulas
• Authors: Jan G\'orecki
• Abstract summary: An example Monte Carlo simulation based application for dependence modeling with copulas is developed using a state-of-the-art large language model (LLM) This includes interaction with ChatGPT in natural language and using mathematical formalism, which led to producing a working code in Python and R. Through careful prompt engineering, we separate successful solutions generated by ChatGPT from unsuccessful ones, resulting in a comprehensive list of related pros and cons.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 using a state-of-the-art large language model (LLM) fine-tuned for conversations. This includes interaction with ChatGPT in natural language and using mathematical formalism, which, under careful supervision by a human-expert, led to producing a working code in MATLAB, Python and R for sampling from a given copula model, evaluation of the model's density, performing maximum likelihood estimation, optimizing the code for parallel computing for CPUs as well as for GPUs, and visualization of 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 solution that is correct. Such ability presents an attractive opportunity to achieve a programmed solution even for users with rather limited knowledge of programming techniques.

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