Extending the Frontier of ChatGPT: Code Generation and Debugging
- URL: http://arxiv.org/abs/2307.08260v1
- Date: Mon, 17 Jul 2023 06:06:58 GMT
- Title: Extending the Frontier of ChatGPT: Code Generation and Debugging
- Authors: Fardin Ahsan Sakib, Saadat Hasan Khan, A. H. M. Rezaul Karim
- Abstract summary: ChatGPT, developed by OpenAI, has ushered in a new era by utilizing artificial intelligence (AI) to tackle diverse problem domains.
This research paper delves into the efficacy of ChatGPT in solving programming problems, examining both the correctness and the efficiency of its solution in terms of time and memory complexity.
The research reveals a commendable overall success rate of 71.875%, denoting the proportion of problems for which ChatGPT was able to provide correct solutions.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large-scale language models (LLMs) have emerged as a groundbreaking
innovation in the realm of question-answering and conversational agents. These
models, leveraging different deep learning architectures such as Transformers,
are trained on vast corpora to predict sentences based on given queries. Among
these LLMs, ChatGPT, developed by OpenAI, has ushered in a new era by utilizing
artificial intelligence (AI) to tackle diverse problem domains, ranging from
composing essays and biographies to solving intricate mathematical integrals.
The versatile applications enabled by ChatGPT offer immense value to users.
However, assessing the performance of ChatGPT's output poses a challenge,
particularly in scenarios where queries lack clear objective criteria for
correctness. For instance, evaluating the quality of generated essays becomes
arduous and relies heavily on manual labor, in stark contrast to evaluating
solutions to well-defined, closed-ended questions such as mathematical
problems. This research paper delves into the efficacy of ChatGPT in solving
programming problems, examining both the correctness and the efficiency of its
solution in terms of time and memory complexity. The research reveals a
commendable overall success rate of 71.875\%, denoting the proportion of
problems for which ChatGPT was able to provide correct solutions that
successfully satisfied all the test cases present in Leetcode. It exhibits
strengths in structured problems and shows a linear correlation between its
success rate and problem acceptance rates. However, it struggles to improve
solutions based on feedback, pointing to potential shortcomings in debugging
tasks. These findings provide a compact yet insightful glimpse into ChatGPT's
capabilities and areas for improvement.
Related papers
- Evaluating ChatGPT as a Question Answering System: A Comprehensive
Analysis and Comparison with Existing Models [0.0]
This article scrutinizes ChatGPT as a Question Answering System (QAS)
The primary focus is on evaluating ChatGPT's proficiency in extracting responses from provided paragraphs.
The evaluation highlights hallucinations, where ChatGPT provides responses to questions without available answers in the provided context.
arXiv Detail & Related papers (2023-12-11T08:49:18Z) - Exploring ChatGPT's Capabilities on Vulnerability Management [56.4403395100589]
We explore ChatGPT's capabilities on 6 tasks involving the complete vulnerability management process with a large-scale dataset containing 70,346 samples.
One notable example is ChatGPT's proficiency in tasks like generating titles for software bug reports.
Our findings reveal the difficulties encountered by ChatGPT and shed light on promising future directions.
arXiv Detail & Related papers (2023-11-11T11:01:13Z) - A Critical Review of Large Language Model on Software Engineering: An Example from ChatGPT and Automated Program Repair [19.123640635549524]
Large Language Models (LLMs) have been gaining increasing attention and demonstrated promising performance across a variety of software engineering tasks.
This paper reviews the bug-fixing capabilities of ChatGPT on a clean APR benchmark with different research objectives.
ChatGPT is able to fix 109 out of 151 buggy programs using the basic prompt within 35 independent rounds, outperforming state-of-the-art LLMs CodeT5 and PLBART by 27.5% and 62.4% prediction accuracy.
arXiv Detail & Related papers (2023-10-13T06:11:47Z) - Thought Propagation: An Analogical Approach to Complex Reasoning with Large Language Models [62.96551299003463]
We propose textbftextitThought Propagation (TP) to enhance the complex reasoning ability of Large Language Models.
TP first prompts LLMs to propose and solve a set of analogous problems that are related to the input one.
TP reuses the results of analogous problems to directly yield a new solution or derive a knowledge-intensive plan for execution to amend the initial solution obtained from scratch.
arXiv Detail & Related papers (2023-10-06T01:40:09Z) - Unmasking the giant: A comprehensive evaluation of ChatGPT's proficiency in coding algorithms and data structures [0.6990493129893112]
We evaluate ChatGPT's ability to generate correct solutions to the problems fed to it, its code quality, and nature of run-time errors thrown by its code.
We look into patterns in the test cases passed in order to gain some insights into how wrong ChatGPT code is in these kinds of situations.
arXiv Detail & Related papers (2023-07-10T08:20:34Z) - A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark
Datasets [19.521390684403293]
We present a thorough evaluation of ChatGPT's performance on diverse academic datasets.
Specifically, we evaluate ChatGPT across 140 tasks and analyze 255K responses it generates in these datasets.
arXiv Detail & Related papers (2023-05-29T12:37:21Z) - ChatGPT-Crawler: Find out if ChatGPT really knows what it's talking
about [15.19126287569545]
This research examines the responses generated by ChatGPT from different Conversational QA corpora.
The study employed BERT similarity scores to compare these responses with correct answers and obtain Natural Language Inference(NLI) labels.
The study identified instances where ChatGPT provided incorrect answers to questions, providing insights into areas where the model may be prone to error.
arXiv Detail & Related papers (2023-04-06T18:42:47Z) - To ChatGPT, or not to ChatGPT: That is the question! [78.407861566006]
This study provides a comprehensive and contemporary assessment of the most recent techniques in ChatGPT detection.
We have curated a benchmark dataset consisting of prompts from ChatGPT and humans, including diverse questions from medical, open Q&A, and finance domains.
Our evaluation results demonstrate that none of the existing methods can effectively detect ChatGPT-generated content.
arXiv Detail & Related papers (2023-04-04T03:04:28Z) - Consistency Analysis of ChatGPT [65.268245109828]
This paper investigates the trustworthiness of ChatGPT and GPT-4 regarding logically consistent behaviour.
Our findings suggest that while both models appear to show an enhanced language understanding and reasoning ability, they still frequently fall short of generating logically consistent predictions.
arXiv Detail & Related papers (2023-03-11T01:19:01Z) - Is ChatGPT a General-Purpose Natural Language Processing Task Solver? [113.22611481694825]
Large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot.
Recently, the debut of ChatGPT has drawn a great deal of attention from the natural language processing (NLP) community.
It is not yet known whether ChatGPT can serve as a generalist model that can perform many NLP tasks zero-shot.
arXiv Detail & Related papers (2023-02-08T09:44:51Z) - A Categorical Archive of ChatGPT Failures [47.64219291655723]
ChatGPT, developed by OpenAI, has been trained using massive amounts of data and simulates human conversation.
It has garnered significant attention due to its ability to effectively answer a broad range of human inquiries.
However, a comprehensive analysis of ChatGPT's failures is lacking, which is the focus of this study.
arXiv Detail & Related papers (2023-02-06T04:21:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.