Across Programming Language Silos: A Study on Cross-Lingual Retrieval-augmented Code Generation

• URL: http://arxiv.org/abs/2506.03535v1
• Date: Wed, 04 Jun 2025 03:31:00 GMT
• Title: Across Programming Language Silos: A Study on Cross-Lingual Retrieval-augmented Code Generation
• Authors: Qiming Zhu, Jialun Cao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, Shing-Chi Cheung,
• Abstract summary: Multi-lingual RACG systems are valuable for migrating code-bases across programming languages.<n>We construct a dataset spanning 13 PLs with nearly 14k instances to explore utility and robustness of multi-lingual RACG systems.
• Score: 48.07804537257056
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current research on large language models (LLMs) with retrieval-augmented code generation (RACG) mainly focuses on single-language settings, leaving cross-lingual effectiveness and security unexplored. Multi-lingual RACG systems are valuable for migrating code-bases across programming languages (PLs), yet face risks from error (e.g. adversarial data corruption) propagation in cross-lingual transfer. We construct a dataset spanning 13 PLs with nearly 14k instances to explore utility and robustness of multi-lingual RACG systems. Our investigation reveals four key insights: (1) Effectiveness: multi-lingual RACG significantly enhances multi-lingual code LLMs generation; (2) Inequality: Java demonstrate superior cross-lingual utility over Python in RACG; (3) Robustness: Adversarial attacks degrade performance significantly in mono-lingual RACG but show mitigated impacts in cross-lingual scenarios; Counterintuitively, perturbed code may improve RACG in cross-lingual scenarios; (4) Specialization: Domain-specific code retrievers outperform significantly general text retrievers. These findings establish foundation for developing effective and secure multi-lingual code assistants.

Related papers

• The Cross-Lingual Cost: Retrieval Biases in RAG over Arabic-English Corpora [6.594531626178451]
  Cross-lingual retrieval-augmented generation (RAG) is a critical capability for retrieving and generating answers across languages.<n>We study Arabic-English RAG in a domain-specific setting using benchmarks derived from real-world corporate datasets.<n>We propose a simple retrieval strategy that addresses this source of failure by enforcing equal retrieval from both languages.
  arXiv  Detail & Related papers  (2025-07-10T08:38:31Z)
• SwitchLingua: The First Large-Scale Multilingual and Multi-Ethnic Code-Switching Dataset [34.40254709148148]
  Code-Switching (CS) is the alternating use of two or more languages within a conversation or utterance.<n>This linguistic phenomenon poses challenges for Automatic Speech Recognition (ASR) systems.<n>textbfSwitchLingua is the first large-scale multilingual and multi-ethnic code-switching dataset.
  arXiv  Detail & Related papers  (2025-05-30T05:54:46Z)
• Cross-Lingual Pitfalls: Automatic Probing Cross-Lingual Weakness of Multilingual Large Language Models [55.14276067678253]
  This paper introduces a novel methodology for efficiently identifying inherent cross-lingual weaknesses in Large Language Models (LLMs)<n>We construct a new dataset of over 6,000 bilingual pairs across 16 languages using this methodology, demonstrating its effectiveness in revealing weaknesses even in state-of-the-art models.<n>Further experiments investigate the relationship between linguistic similarity and cross-lingual weaknesses, revealing that linguistically related languages share similar performance patterns.
  arXiv  Detail & Related papers  (2025-05-24T12:31:27Z)
• MrGuard: A Multilingual Reasoning Guardrail for Universal LLM Safety [56.79292318645454]
  Large Language Models (LLMs) are susceptible to adversarial attacks such as jailbreaking.<n>This vulnerability is exacerbated in multilingual settings, where multilingual safety-aligned data is often limited.<n>We introduce a multilingual guardrail with reasoning for prompt classification.
  arXiv  Detail & Related papers  (2025-04-21T17:15:06Z)
• Multilingual Retrieval-Augmented Generation for Knowledge-Intensive Task [73.35882908048423]
  Retrieval-augmented generation (RAG) has become a cornerstone of contemporary NLP.<n>This paper investigates the effectiveness of RAG across multiple languages by proposing novel approaches for multilingual open-domain question-answering.
  arXiv  Detail & Related papers  (2025-04-04T17:35:43Z)
• Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models [62.91524967852552]
  Large language models (LLMs) are typically multilingual due to pretraining on diverse multilingual corpora.<n>But can these models relate corresponding concepts across languages, i.e., be crosslingual?<n>This study evaluates state-of-the-art LLMs on inherently crosslingual tasks.
  arXiv  Detail & Related papers  (2024-06-23T15:15:17Z)
• Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual Understanding [10.154013836043816]
  Code-switching in red-teaming queries can effectively elicit undesirable behaviors of large language models (LLMs) We introduce a simple yet effective framework, CSRT, to synthesize code-switching red-teaming queries. We demonstrate that the CSRT significantly outperforms existing multilingual red-teaming techniques.
  arXiv  Detail & Related papers  (2024-06-17T06:08:18Z)
• Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection [19.399281609371258]
  Cross-lingual transfer learning from high-resource to medium and low-resource languages has shown encouraging results. We resort to data augmentation and continual pre-training for domain adaptation to improve cross-lingual abusive language detection.
  arXiv  Detail & Related papers  (2023-11-03T16:51:07Z)
• Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
  We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally. Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
  arXiv  Detail & Related papers  (2023-06-13T08:08:08Z)

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.