TaskComplexity: A Dataset for Task Complexity Classification with In-Context Learning, FLAN-T5 and GPT-4o Benchmarks

TL;DR

This paper introduces a new dataset of 4,112 programming tasks for classifying task complexity, and benchmarks the performance of FLAN-T5 and GPT-4o models using in-context learning and fine-tuning.

Contribution

It presents a novel dataset for programming task complexity classification and evaluates the effectiveness of in-context learning versus fine-tuning approaches.

Findings

GPT-4o-mini outperforms FLAN-T5 in classification accuracy

The dataset covers diverse programming problems from various sources

In-context learning proves effective for task complexity classification

Abstract

This paper addresses the challenge of classifying and assigning programming tasks to experts, a process that typically requires significant effort, time, and cost. To tackle this issue, a novel dataset containing a total of 4,112 programming tasks was created by extracting tasks from various websites. Web scraping techniques were employed to collect this dataset of programming problems systematically. Specific HTML tags were tracked to extract key elements of each issue, including the title, problem description, input-output, examples, problem class, and complexity score. Examples from the dataset are provided in the appendix to illustrate the variety and complexity of tasks included. The dataset's effectiveness has been evaluated and benchmarked using two approaches; the first approach involved fine-tuning the FLAN-T5 small model on the dataset, while the second approach used in-context learning (ICL) with the GPT-4o mini. The performance was assessed using standard metrics: accuracy, recall, precision, and F1-score. The results indicated that in-context learning with GPT-4o-mini outperformed the FLAN-T5 model.