Comparing large language models and human programmers for generating programming code

• URL: http://arxiv.org/abs/2403.00894v2
• Date: Sat, 05 Oct 2024 00:34:44 GMT
• Title: Comparing large language models and human programmers for generating programming code
• Authors: Wenpin Hou, Zhicheng Ji,
• Abstract summary: GPT-4 substantially outperforms other large language models, including Gemini Ultra and Claude 2. In most LeetCode and GeeksforGeeks coding contests evaluated in this study, GPT-4 employing the optimal prompt strategy outperforms 85 percent of human participants.
• Score: 0.0
• License:
• Abstract: We systematically evaluated the performance of seven large language models in generating programming code using various prompt strategies, programming languages, and task difficulties. GPT-4 substantially outperforms other large language models, including Gemini Ultra and Claude 2. The coding performance of GPT-4 varies considerably with different prompt strategies. In most LeetCode and GeeksforGeeks coding contests evaluated in this study, GPT-4 employing the optimal prompt strategy outperforms 85 percent of human participants. Additionally, GPT-4 demonstrates strong capabilities in translating code between different programming languages and in learning from past errors. The computational efficiency of the code generated by GPT-4 is comparable to that of human programmers. These results suggest that GPT-4 has the potential to serve as a reliable assistant in programming code generation and software development.

Related papers

• CodeGRAG: Bridging the Gap between Natural Language and Programming Language via Graphical Retrieval Augmented Generation [58.84212778960507]
  We propose CodeGRAG, a Graphical Retrieval Augmented Code Generation framework to enhance the performance of LLMs. CodeGRAG builds the graphical view of code blocks based on the control flow and data flow of them to fill the gap between programming languages and natural language. Various experiments and ablations are done on four datasets including both the C++ and python languages to validate the hard meta-graph prompt, the soft prompting technique, and the effectiveness of the objectives for pretrained GNN expert.
  arXiv  Detail & Related papers  (2024-05-03T02:48:55Z)
• Evaluating the Application of Large Language Models to Generate Feedback in Programming Education [0.0]
  This study investigates the application of large language models, specifically GPT-4, to enhance programming education. The research outlines the design of a web application that uses GPT-4 to provide feedback on programming tasks, without giving away the solution.
  arXiv  Detail & Related papers  (2024-03-13T23:14:35Z)
• OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement [58.034012276819425]
  We introduce OpenCodeInterpreter, a family of open-source code systems for generating, executing, and iteratively refining code. Our comprehensive evaluation of OpenCodeInterpreter across key benchmarks such as HumanEval, MBPP, and their enhanced versions from EvalPlus reveals its exceptional performance.
  arXiv  Detail & Related papers  (2024-02-22T16:06:23Z)
• CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model [58.127534002232096]
  This paper introduces CodeFuse-13B, an open-sourced pre-trained code LLM. It is specifically designed for code-related tasks with both English and Chinese prompts. CodeFuse achieves its effectiveness by utilizing a high quality pre-training dataset.
  arXiv  Detail & Related papers  (2023-10-10T02:38:44Z)
• OpenAi's GPT4 as coding assistant [0.0]
  GPT4 is considered the most potent Large Language Model from Openai. In this paper, we examine GPT3.5 and GPT4 as coding assistants.
  arXiv  Detail & Related papers  (2023-09-22T09:31:39Z)
• Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors [21.227955181065948]
  We systematically evaluate two models, ChatGPT (based on GPT-3.5) and GPT-4, and compare their performance with human tutors for a variety of scenarios. Our results show that GPT-4 drastically outperforms ChatGPT (based on GPT-3.5) and comes close to human tutors' performance for several scenarios.
  arXiv  Detail & Related papers  (2023-06-29T17:57:40Z)
• Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses [0.0]
  GPT models evolved from completely failing the typical programming class' assessments to confidently passing the courses with no human involvement. This study provides evidence that programming instructors need to prepare for a world in which there is an easy-to-use technology that can be utilized by learners to collect passing scores.
  arXiv  Detail & Related papers  (2023-06-15T22:12:34Z)
• Generalized Planning in PDDL Domains with Pretrained Large Language Models [82.24479434984426]
  We consider PDDL domains and use GPT-4 to synthesize Python programs. We evaluate this approach in seven PDDL domains and compare it to four ablations and four baselines.
  arXiv  Detail & Related papers  (2023-05-18T14:48:20Z)
• AI-assisted coding: Experiments with GPT-4 [0.22366638308792727]
  GPT-4 can generate tests with substantial coverage, but that many of the tests fail applied to the associated code. These findings suggest that while AI coding tools are very powerful, they still require humans in the loop to ensure validity and accuracy of the results.
  arXiv  Detail & Related papers  (2023-04-25T22:59:01Z)
• Visual Instruction Tuning [79.70923292053097]
  We present the first attempt to use language-only GPT-4 to generate multimodal language-image instruction-following data. By instruction tuning on such generated data, we introduce LLaVA: Large Language and Vision Assistant. When fine-tuned on Science QA, the synergy of LLaVA and GPT-4 achieves a new state-of-the-art accuracy of 92.53%.
  arXiv  Detail & Related papers  (2023-04-17T17:59:25Z)
• GPT-4 Technical Report [116.90398195245983]
  GPT-4 is a large-scale, multimodal model which can accept image and text inputs and produce text outputs. It exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers.
  arXiv  Detail & Related papers  (2023-03-15T17:15:04Z)

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.