Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners

• URL: http://arxiv.org/abs/2412.12800v1
• Date: Tue, 17 Dec 2024 11:06:02 GMT
• Title: Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners
• Authors: James Prather, Brent N. Reeves, Paul Denny, Juho Leinonen, Stephen MacNeil, Andrew Luxton-Reilly, João Orvalho, Amin Alipour, Ali Alfageeh, Thezyrie Amarouche, Bailey Kimmel, Jared Wright, Musa Blake, Gweneth Barbre,
• Abstract summary: Non-native English speakers (NNES) face multiple barriers to learning programming. Advances in generative AI (GenAI) have the potential to break down these barriers. In this paper, we provide the first exploration of NNES students prompting in their native languages to generate code.
• Score: 3.1550561074143597
• License:
• Abstract: Non-native English speakers (NNES) face multiple barriers to learning programming. These barriers can be obvious, such as the fact that programming language syntax and instruction are often in English, or more subtle, such as being afraid to ask for help in a classroom full of native English speakers. However, these barriers are frustrating because many NNES students know more about programming than they can articulate in English. Advances in generative AI (GenAI) have the potential to break down these barriers because state of the art models can support interactions in multiple languages. Moreover, recent work has shown that GenAI can be highly accurate at code generation and explanation. In this paper, we provide the first exploration of NNES students prompting in their native languages (Arabic, Chinese, and Portuguese) to generate code to solve programming problems. Our results show that students are able to successfully use their native language to solve programming problems, but not without some difficulty specifying programming terminology and concepts. We discuss the challenges they faced, the implications for practice in the short term, and how this might transform computing education globally in the long term.

Related papers

• Integrating Natural Language Prompting Tasks in Introductory Programming Courses [3.907735250728617]
  This report explores the inclusion of two prompt-focused activities in an introductory programming course. The first requires students to solve computational problems by writing natural language prompts, emphasizing problem-solving over syntax. The second involves students crafting prompts to generate code equivalent to provided fragments, to foster an understanding of the relationship between prompts and code.
  arXiv  Detail & Related papers  (2024-10-04T01:03:25Z)
• Prompts First, Finally [4.5022979431802925]
  Generative AI (GenAI) and large language models in particular, are disrupting Computer Science Education. Some educators argue that they pose a serious threat to computing education, and that we should ban their use in the classroom. We argue that our programming abstractions were always headed here -- to natural language.
  arXiv  Detail & Related papers  (2024-07-12T12:50:28Z)
• 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)
• No Train but Gain: Language Arithmetic for training-free Language Adapters enhancement [59.37775534633868]
  We introduce a novel method called language arithmetic, which enables training-free post-processing. The effectiveness of the proposed solution is demonstrated on three downstream tasks in a MAD-X-based set of cross-lingual schemes.
  arXiv  Detail & Related papers  (2024-04-24T08:52:40Z)
• Interactions with Prompt Problems: A New Way to Teach Programming with Large Language Models [4.1599514827277355]
  We propose a new way to teach programming with Prompt Problems. Students receive a problem visually, indicating how input should be transformed to output, and must translate that to a prompt for an LLM to decipher. The problem is considered correct when the code that is generated by the student prompt can pass all test cases.
  arXiv  Detail & Related papers  (2024-01-19T15:32:46Z)
• Will Code Remain a Relevant User Interface for End-User Programming with Generative AI Models? [20.275891144535258]
  We explore the extent to which "traditional" programming languages remain relevant for non-expert end-user programmers in a world with generative AI. We outline some reasons that traditional programming languages may still be relevant and useful for end-user programmers.
  arXiv  Detail & Related papers  (2023-11-01T09:20:21Z)
• InstructAlign: High-and-Low Resource Language Alignment via Continual Crosslingual Instruction Tuning [66.31509106146605]
  Large language models (LLMs) that are tuned with instructions have demonstrated remarkable capabilities in various tasks and languages. However, their ability to generalize to underrepresented languages is limited due to the scarcity of available data. We propose InstructAlign which uses continual crosslingual instruction tuning to enable LLMs to align new unseen languages with previously learned high-resource languages.
  arXiv  Detail & Related papers  (2023-05-23T02:51:34Z)
• MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages [76.93265104421559]
  We benchmark code generation from natural language commands extending beyond English. We annotated a total of 896 NL-code pairs in three languages: Spanish, Japanese, and Russian. While the difficulties vary across these three languages, all systems lag significantly behind their English counterparts.
  arXiv  Detail & Related papers  (2022-03-16T04:21:50Z)
• LISA: Learning Interpretable Skill Abstractions from Language [85.20587800593293]
  We propose a hierarchical imitation learning framework that can learn diverse, interpretable skills from language-conditioned demonstrations. Our method demonstrates a more natural way to condition on language in sequential decision-making problems.
  arXiv  Detail & Related papers  (2022-02-28T19:43:24Z)
• DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning [47.910312960048174]
  We present DreamCoder, a system that learns to solve problems by writing programs. It builds expertise by creating programming languages for expressing domain concepts, together with neural networks. A wake-sleep'' learning algorithm alternately extends the language with new symbolic abstractions and trains the neural network on imagined and replayed problems.
  arXiv  Detail & Related papers  (2020-06-15T13:06:29Z)

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.