LLMs Love Python: A Study of LLMs' Bias for Programming Languages and Libraries

• URL: http://arxiv.org/abs/2503.17181v1
• Date: Fri, 21 Mar 2025 14:29:35 GMT
• Title: LLMs Love Python: A Study of LLMs' Bias for Programming Languages and Libraries
• Authors: Lukas Twist, Jie M. Zhang, Mark Harman, Don Syme, Joost Noppen, Detlef Nauck,
• Abstract summary: Large Language Models (LLMs) play an increasing role in code generation.<n>This study provides the first in-depth investigation into LLM preferences for programming languages and libraries used when generating code.<n>Our findings reveal that LLMs heavily favour Python when solving language-agnostic problems.
• Score: 15.140178992235123
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Programming language and library choices are crucial to software reliability and security. Poor or inconsistent choices can lead to increased technical debt, security vulnerabilities, and even catastrophic failures in safety-critical systems. As Large Language Models (LLMs) play an increasing role in code generation, it is essential to understand how they make these decisions. However, little is known about their preferences when selecting programming languages and libraries for different coding tasks. To fill this gap, this study provides the first in-depth investigation into LLM preferences for programming languages and libraries used when generating code. We assess the preferences of eight diverse LLMs by prompting them to complete various coding tasks, including widely-studied benchmarks and the more practical task of generating the initial structural code for new projects (a crucial step that often determines a project's language or library choices). Our findings reveal that LLMs heavily favour Python when solving language-agnostic problems, using it in 90%-97% of cases for benchmark tasks. Even when generating initial project code where Python is not a suitable language, it remains the most-used language in 58% of instances. Moreover, LLMs contradict their own language recommendations in 83% of project initialisation tasks, raising concerns about their reliability in guiding language selection. Similar biases toward well-established libraries further create serious discoverability challenges for newer open-source projects. These results highlight the need to improve LLMs' adaptability to diverse programming contexts and to develop mechanisms for mitigating programming language and library bias.

Related papers

• Large Language Model Unlearning for Source Code [65.42425213605114]
  PROD is a novel unlearning approach that enables LLMs to forget undesired code content while preserving their code generation capabilities.<n>Our evaluation demonstrates that PROD achieves superior balance between forget quality and model utility compared to existing unlearning approaches.
  arXiv  Detail & Related papers  (2025-06-20T16:27:59Z)
• Evaluating Programming Language Confusion [6.462594894731934]
  Large Language Models for code (Code LLMs) have gained significant traction in software engineering.<n>These models have demonstrated remarkable capabilities in understanding programming concepts, implementing algorithms, and even bridging different programming languages.<n>Despite these advances, Code LLMs often struggle with programming language confusion--producing code in unintended languages.
  arXiv  Detail & Related papers  (2025-03-17T18:14:15Z)
• Isolating Language-Coding from Problem-Solving: Benchmarking LLMs with PseudoEval [7.33924106492889]
  Existing code generation benchmarks are designed to study Large Language Models' end-to-end performance.<n>We construct PseudoEval, a multilingual code generation benchmark that provides a solution written in pseudocode as input.<n>Our study indicates that problem-solving capability may transfer across programming languages, while language-coding needs more language-specific effort.
  arXiv  Detail & Related papers  (2025-02-26T14:08:17Z)
• Enhancing Code Generation for Low-Resource Languages: No Silver Bullet [55.39571645315926]
  Large Language Models (LLMs) rely on large and diverse datasets to learn syntax, semantics, and usage patterns of programming languages.<n>For low-resource languages, the limited availability of such data hampers the models' ability to generalize effectively.<n>We present an empirical study investigating the effectiveness of several approaches for boosting LLMs' performance on low-resource languages.
  arXiv  Detail & Related papers  (2025-01-31T12:23:28Z)
• Crystal: Illuminating LLM Abilities on Language and Code [58.5467653736537]
  We propose a pretraining strategy to enhance the integration of natural language and coding capabilities. The resulting model, Crystal, demonstrates remarkable capabilities in both domains.
  arXiv  Detail & Related papers  (2024-11-06T10:28:46Z)
• Codellm-Devkit: A Framework for Contextualizing Code LLMs with Program Analysis Insights [9.414198519543564]
  We present codellm-devkit (hereafter, CLDK'), an open-source library that significantly simplifies the process of performing program analysis. CLDK offers developers an intuitive and user-friendly interface, making it incredibly easy to provide rich program analysis context to code LLMs.
  arXiv  Detail & Related papers  (2024-10-16T20:05:59Z)
• Leveraging Open-Source Large Language Models for Native Language Identification [1.6267479602370543]
  Native Language Identification (NLI) has applications in forensics, marketing, and second language acquisition.<n>This study explores the potential of using open-source generative large language models (LLMs) for NLI.
  arXiv  Detail & Related papers  (2024-09-15T08:14:18Z)
• CRUXEval-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution [50.7413285637879]
  The CRUXEVAL-X code reasoning benchmark contains 19 programming languages. It comprises at least 600 subjects for each language, along with 19K content-consistent tests in total. Even a model trained solely on Python can achieve at most 34.4% Pass@1 in other languages.
  arXiv  Detail & Related papers  (2024-08-23T11:43:00Z)
• Perplexed: Understanding When Large Language Models are Confused [3.4208414448496027]
  This paper introduces perplexed, a library for exploring where a language model is perplexed. We conducted a case study focused on Large Language Models (LLMs) for code generation using an additional tool we built to help with the analysis of code models called codetokenizer. We found that our studied code LLMs had their worst performance on coding structures where the code was not syntactically correct.
  arXiv  Detail & Related papers  (2024-04-09T22:03:39Z)
• 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)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Learning Transfers over Several Programming Languages [5.350495525141013]
  Cross-lingual transfer uses data from a source language to improve model performance on a target language. This paper reports extensive experiments on four tasks using a transformer-based large language model and 11 to 41 programming languages. We find that learning transfers well across several programming languages.
  arXiv  Detail & Related papers  (2023-10-25T19:04:33Z)
• Large Language Model-Aware In-Context Learning for Code Generation [75.68709482932903]
  Large language models (LLMs) have shown impressive in-context learning (ICL) ability in code generation. We propose a novel learning-based selection approach named LAIL (LLM-Aware In-context Learning) for code generation.
  arXiv  Detail & Related papers  (2023-10-15T06:12:58Z)
• 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)
• The potential of LLMs for coding with low-resource and domain-specific programming languages [0.0]
  This study focuses on the econometric scripting language named hansl of the open-source software gretl. Our findings suggest that LLMs can be a useful tool for writing, understanding, improving, and documenting gretl code.
  arXiv  Detail & Related papers  (2023-07-24T17:17:13Z)
• LEVER: Learning to Verify Language-to-Code Generation with Execution [64.36459105535]
  We propose LEVER, a simple approach to improve language-to-code generation by learning to verify the generated programs with their execution results. Specifically, we train verifiers to determine whether a program sampled from the LLMs is correct or not based on the natural language input, the program itself and its execution results. LEVER consistently improves over the base code LLMs(4.6% to 10.9% with code-davinci) and achieves new state-of-the-art results on all of them.
  arXiv  Detail & Related papers  (2023-02-16T18:23:22Z)
• Leveraging Language to Learn Program Abstractions and Search Heuristics [66.28391181268645]
  We introduce LAPS (Language for Abstraction and Program Search), a technique for using natural language annotations to guide joint learning of libraries and neurally-guided search models for synthesis. When integrated into a state-of-the-art library learning system (DreamCoder), LAPS produces higher-quality libraries and improves search efficiency and generalization.
  arXiv  Detail & Related papers  (2021-06-18T15:08:47Z)

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.