A Survey On Large Language Models For Code Generation

• URL: http://arxiv.org/abs/2503.01245v1
• Date: Mon, 03 Mar 2025 07:17:30 GMT
• Title: A Survey On Large Language Models For Code Generation
• Authors: Nam Huynh, Beiyu Lin,
• Abstract summary: Large Language Models (LLMs) have demonstrated their remarkable capabilities in numerous fields.<n>This survey focuses on how LLMs empower users, regardless of their technical background, to use human languages to automatically generate executable code.
• Score: 0.9105696129628794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated their remarkable capabilities in numerous fields. This survey focuses on how LLMs empower users, regardless of their technical background, to use human languages to automatically generate executable code. We begin with understanding LLMs' limitations and challenges in automated code generation. Subsequently, we review various fine-tuning techniques designed to enhance both the performance and adaptability of LLMs in code generation tasks. We then review the existing metrics and benchmarks for evaluations to assess model performance based on fine-tuning techniques. Finally, we explore the applications of LLMs (e.g. CodeLlama, GitHub Copilot, ToolGen) in code generation tasks to illustrate their roles and functionalities. This survey provides a comprehensive overview of LLMs for code generation, helps researchers in diverse fields better understand the current state-of-the-art technologies, and offers the potential of effectively leveraging LLMs for code generation tasks.

Related papers

• Ensemble Learning for Large Language Models in Text and Code Generation: A Survey [6.041894045506043]
  We focus on four methods and models that show strong performance and potential for broader applications. These include better representation of diversity, improved output quality, and greater flexibility in applications.
  arXiv  Detail & Related papers  (2025-03-13T18:50:57Z)
• CodeIF: Benchmarking the Instruction-Following Capabilities of Large Language Models for Code Generation [24.090719826360342]
  We introduce CodeIF, the first benchmark designed to assess the abilities of Large Language Models (LLMs) to adhere to task-oriented instructions within code generation scenarios. We conduct extensive experiments with LLMs, analyzing their strengths and limitations in meeting the demands of these tasks.
  arXiv  Detail & Related papers  (2025-02-26T14:19:49Z)
• From Selection to Generation: A Survey of LLM-based Active Learning [153.8110509961261]
  Large Language Models (LLMs) have been employed for generating entirely new data instances and providing more cost-effective annotations. This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques.
  arXiv  Detail & Related papers  (2025-02-17T12:58:17Z)
• Multi-Programming Language Sandbox for LLMs [78.99934332554963]
  out-of-the-box multi-programming language sandbox designed to provide unified and comprehensive feedback from compiler and analysis tools for Large Language Models (LLMs) It can automatically identify the programming language of the code, compiling and executing it within an isolated sub-sandbox to ensure safety and stability.
  arXiv  Detail & Related papers  (2024-10-30T14:46:43Z)
• A Survey on Evaluating Large Language Models in Code Generation Tasks [30.256255254277914]
  This paper provides a comprehensive review of the current methods and metrics used to evaluate the performance of Large Language Models (LLMs) in code generation tasks.<n>With the rapid growth in demand for automated software development, LLMs have demonstrated significant potential in the field of code generation.
  arXiv  Detail & Related papers  (2024-08-29T12:56:06Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• A Survey on Large Language Models for Code Generation [9.555952109820392]
  Large Language Models (LLMs) have garnered remarkable advancements across diverse code-related tasks. This survey aims to bridge the gap between academia and practical development by providing a comprehensive and up-to-date literature review.
  arXiv  Detail & Related papers  (2024-06-01T17:48:15Z)
• InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models [56.723509505549536]
  InfiBench is the first large-scale freeform question-answering (QA) benchmark for code to our knowledge. It comprises 234 carefully selected high-quality Stack Overflow questions that span across 15 programming languages. We conduct a systematic evaluation for over 100 latest code LLMs on InfiBench, leading to a series of novel and insightful findings.
  arXiv  Detail & Related papers  (2024-03-11T02:06:30Z)
• Large Language Models: A Survey [66.39828929831017]
  Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks. LLMs' ability of general-purpose language understanding and generation is acquired by training billions of model's parameters on massive amounts of text data.
  arXiv  Detail & Related papers  (2024-02-09T05:37:09Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)

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.