A Multi-Language Perspective on the Robustness of LLM Code Generation

• URL: http://arxiv.org/abs/2504.19108v1
• Date: Sun, 27 Apr 2025 05:00:21 GMT
• Title: A Multi-Language Perspective on the Robustness of LLM Code Generation
• Authors: Fazle Rabbi, Zushuo Ding, Jinqiu Yang,
• Abstract summary: We conduct a comprehensive comparative analysis to assess the robustness of several prominent code generation models.<n>We introduce perturbations in four key areas of the prompt: DocString, function name, syntax, and format.<n>This work presents our experimental findings, shedding light on the performance of code generation models in various scenarios.
• Score: 2.580765958706854
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models have gained significant traction and popularity in recent times, extending their usage to code-generation tasks. While this field has garnered considerable attention, the exploration of testing and evaluating the robustness of code generation models remains an ongoing endeavor. Previous studies have primarily focused on code generation models specifically for the Python language, overlooking other widely used programming languages. In this research, we conduct a comprehensive comparative analysis to assess the robustness performance of several prominent code generation models. Furthermore, we investigate how their performance varies across different programming languages. To accomplish this, we introduce perturbations in four key areas of the prompt: DocString, function name, syntax, and format. We have compiled and released a dedicated dataset for this purpose. This work presents our experimental findings, shedding light on the performance of code generation models in various scenarios.

Related papers

• CodeXEmbed: A Generalist Embedding Model Family for Multiligual and Multi-task Code Retrieval [103.116634967815]
  We introduce CodeXEmbed, a family of large-scale code embedding models ranging from 400M to 7B parameters. Our novel training pipeline unifies multiple programming languages and transforms various code-related tasks into a common retrieval framework. Our 7B model sets a new state-of-the-art (SOTA) in code retrieval, outperforming the previous leading model, Voyage-Code, by over 20% on CoIR benchmark.
  arXiv  Detail & Related papers  (2024-11-19T16:54:45Z)
• P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs [84.24644520272835]
  Large language models (LLMs) showcase varied multilingual capabilities across tasks like translation, code generation, and reasoning. Previous assessments often limited their scope to fundamental natural language processing (NLP) or isolated capability-specific tasks. We present a pipeline for selecting available and reasonable benchmarks from massive ones, addressing the oversight in previous work regarding the utility of these benchmarks. We introduce P-MMEval, a large-scale benchmark covering effective fundamental and capability-specialized datasets.
  arXiv  Detail & Related papers  (2024-11-14T01:29:36Z)
• Can OpenSource beat ChatGPT? -- A Comparative Study of Large Language Models for Text-to-Code Generation [0.24578723416255752]
  We evaluate five different large language models (LLMs) concerning their capabilities for text-to-code generation. ChatGPT can handle these typical programming challenges by far the most effectively, surpassing even code-specialized models like Code Llama.
  arXiv  Detail & Related papers  (2024-09-06T10:03:49Z)
• Large Language Models for Code Summarization [0.0]
  We review how Large Language Models perform in code explanation/summarization. We also investigate their code generation capabilities based on natural language descriptions.
  arXiv  Detail & Related papers  (2024-05-29T12:18:51Z)
• GenCodeSearchNet: A Benchmark Test Suite for Evaluating Generalization in Programming Language Understanding [5.9535699822923]
  We propose a new benchmark dataset called GenCodeSearchNet (GeCS) to evaluate the programming language understanding capabilities of language models. As part of the full dataset, we introduce a new, manually curated subset StatCodeSearch that focuses on R, a popular but so far underrepresented programming language. For evaluation and comparison, we collect several baseline results using fine-tuned BERT-style models and GPT-style large language models.
  arXiv  Detail & Related papers  (2023-11-16T09:35:00Z)
• Evaluating Large Language Models on Controlled Generation Tasks [92.64781370921486]
  We present an extensive analysis of various benchmarks including a sentence planning benchmark with different granularities. After comparing large language models against state-of-the-start finetuned smaller models, we present a spectrum showing large language models falling behind, are comparable, or exceed the ability of smaller models.
  arXiv  Detail & Related papers  (2023-10-23T03:48:24Z)
• L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models [102.00201523306986]
  We present L2CEval, a systematic evaluation of the language-to-code generation capabilities of large language models (LLMs) We analyze the factors that potentially affect their performance, such as model size, pretraining data, instruction tuning, and different prompting methods. In addition to assessing model performance, we measure confidence calibration for the models and conduct human evaluations of the output programs.
  arXiv  Detail & Related papers  (2023-09-29T17:57:00Z)
• A Simple, Yet Effective Approach to Finding Biases in Code Generation [16.094062131137722]
  This work shows that current code generation systems exhibit undesired biases inherited from their large language model backbones. We propose the "block of influence" concept, which enables a modular decomposition and analysis of the coding challenges.
  arXiv  Detail & Related papers  (2022-10-31T15:06:15Z)
• Multi-lingual Evaluation of Code Generation Models [82.7357812992118]
  We present new benchmarks on evaluation code generation models: MBXP and Multilingual HumanEval, and MathQA-X. These datasets cover over 10 programming languages. We are able to assess the performance of code generation models in a multi-lingual fashion.
  arXiv  Detail & Related papers  (2022-10-26T17:17:06Z)
• Twist Decoding: Diverse Generators Guide Each Other [116.20780037268801]
  We introduce Twist decoding, a simple and general inference algorithm that generates text while benefiting from diverse models. Our method does not assume the vocabulary, tokenization or even generation order is shared.
  arXiv  Detail & Related papers  (2022-05-19T01:27:53Z)

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.