Boosting Automatic Java-to-Cangjie Translation with Multi-Stage LLM Training and Error Repair

• URL: http://arxiv.org/abs/2605.07403v1
• Date: Fri, 08 May 2026 07:58:12 GMT
• Title: Boosting Automatic Java-to-Cangjie Translation with Multi-Stage LLM Training and Error Repair
• Abstract summary: We propose a training framework that employs the iterative error repair technique to translate Java code into Cangjie code.<n> Experimental results show that, with limited parallel data, our approach improves functional equivalence by 6.06% compared to the state-of-the-art approaches.
• Score: 23.50835504751422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid evolution of emerging programming language ecosystems, the demand for code translation to low-resource languages continues to grow. As Cangjie emerges as a new programming language, its ecosystem and development toolchains are rapidly expanding. Automated translation from popular programming languages to Cangjie is therefore valuable for practical development. However, constrained by both insufficient Cangjie knowledge and scarce parallel code corpora, general Large Language Models (LLMs) are prone to syntactic errors and semantic as well as structural misalignment in code translation. Existing approaches typically rely on fine-tuning with large-scale parallel data, but they cannot reliably improve compilability or semantic consistency for low-resource Cangjie languages. To tackle these challenges, we propose a multi-stage training framework of LLMs that employs the iterative error repair technique to translate Java code into Cangjie code. This training framework performs training on LLMs, gradually integrating knowledge and achieving semantic alignment as well as structure awareness. During the code translation, we also combine the compiler feedback and error repair case retrieval to repair the incorrect Cangjie code. We construct syntactic knowledge and monolingual instruction datasets to train the LLM. In addition, we also build a Cangjie error repair repository to support error repair in our approach. Experimental results show that, with limited parallel data, our approach improves functional equivalence by 6.06\% compared to the state-of-the-art approaches. Meanwhile, ablation studies confirm that each training stage positively contributes to the final performance.

Related papers

• Beyond the Traceback: Using LLMs for Adaptive Explanations of Programming Errors [52.619793817715326]
  We present a multi-stage crowdsourced study evaluating skill-targeted, LLM-generated Python error messages.<n>We show that while LLM-rewritten messages significantly improved subjective evaluations, these perceived gains did not translate into statistically significant improvements in objective debug performance.<n>This highlights a critical human-AI complementarity gap: explanations that feel better to users do not necessarily make them more effective debuggers.
  arXiv  Detail & Related papers  (2026-08-21T09:14:28Z)
• SEDCoT: Enhancing LLM-Based COBOL Code Translation via Symbolic Execution and Delta Debugging [49.898541267264115]
  Maintenance is increasingly challenging due to outdated technologies, sparse documentation, and developer retirement.<n>Traditional rule-based transcompilers yield outputs that are difficult to read and maintain.<n>We propose SEDCoT, a novel-to-C translation framework.
  arXiv  Detail & Related papers  (2026-07-05T03:07:10Z)
• Neural Code Translation of Legacy Code: APL to C# [0.0]
  This paper investigates the translation of APL into C# using large language models.<n>We introduce a novel framework for APL-to-C# translation by comparing three guided strategies.<n>Our results demonstrate that neural code translation can successfully bridge the gap between APL and C# for a wide range of programs.
  arXiv  Detail & Related papers  (2026-05-12T12:11:33Z)
• Compensating Visual Insufficiency with Stratified Language Guidance for Long-Tail Class Incremental Learning [50.795452298625996]
  Long-tail class incremental learning (LT CIL) remains highly challenging because the scarcity of samples in tail classes hampers their learning.<n>We exploit the informativeness and scalability of language knowledge to tackle these issues.<n>We introduce stratified adaptive language guidance, which leverages learnable weights to merge multi-scale semantic representations.
  arXiv  Detail & Related papers  (2026-03-23T08:50:00Z)
• CodeSimpleQA: Scaling Factuality in Code Large Language Models [55.705748501461294]
  We present CodeSimpleQA, a comprehensive benchmark designed to evaluate the factual accuracy of code LLMs in answering code-related questions.<n>We also create CodeSimpleQA-Instruct, a large-scale instruction corpus with 66M samples, and develop a post-training framework combining supervised fine-tuning and reinforcement learning.
  arXiv  Detail & Related papers  (2025-12-22T14:27:17Z)
• From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence [150.3696990310269]
  Large language models (LLMs) have transformed automated software development by enabling direct translation of natural language descriptions into functional code.<n>We provide a comprehensive synthesis and practical guide (a series of analytic and probing experiments) about code LLMs.<n>We analyze the code capability of the general LLMs (GPT-4, Claude, LLaMA) and code-specialized LLMs (StarCoder, Code LLaMA, DeepSeek-Coder, and QwenCoder)
  arXiv  Detail & Related papers  (2025-11-23T17:09:34Z)
• Beyond Language Barriers: Multi-Agent Coordination for Multi-Language Code Generation [8.896718697354187]
  XL-CoGen produces high-quality code across multiple programming languages.<n>It integrates intermediate representation, code generation, translation, and automated repair.
  arXiv  Detail & Related papers  (2025-09-24T09:18:08Z)
• The Unreasonable Effectiveness of Model Merging for Cross-Lingual Transfer in LLMs [45.08958917457921]
  Large language models (LLMs) still struggle across tasks outside of high-resource languages.<n>In this work, we investigate cross-lingual transfer to lower-resource languages where task-specific post-training data is scarce.
  arXiv  Detail & Related papers  (2025-05-23T20:28:31Z)
• Program Semantic Inequivalence Game with Large Language Models [20.43560028315856]
  Large Language Models (LLMs) can achieve strong performance on everyday coding tasks, but they can fail on complex tasks that require non-trivial reasoning about program semantics.<n>In this work, we explore a method to synthetically generate code reasoning training data based on a semantic inequivalence game SInQ.<n>We prove that this setup enables theoretically unlimited improvement through self-play in the limit of infinite computational resources.
  arXiv  Detail & Related papers  (2025-05-02T20:03:35Z)
• Post-Incorporating Code Structural Knowledge into Pretrained Models via ICL for Code Translation [15.73070332657629]
  Large language models (LLMs) have achieved significant advancements in software mining.<n> handling the syntactic structure of source code remains a challenge.<n>This paper employs incontext learning (ICL) to integrate code structural knowledge into pre-trained LLMs.
  arXiv  Detail & Related papers  (2025-03-28T10:59:42Z)
• Scaffolded Language Models with Language Supervision for Mixed-Autonomy: A Survey [52.00674453604779]
  This survey organizes the literature on the design and optimization of emerging structures around post-trained LMs.<n>We refer to this overarching structure as scaffolded LMs and focus on LMs that are integrated into multi-step processes with tools.
  arXiv  Detail & Related papers  (2024-10-21T18:06:25Z)
• Semantic Alignment-Enhanced Code Translation via an LLM-Based Multi-Agent System [24.52067108242477]
  Code translation is crucial for software migration, system ablation, and cross-platform development.<n>Traditional rule-based methods rely on manually-written rules, which can be time-consuming and often result in less readable code.<n>More recently, the advance of Large Language Models (LLMs) further boosts learning-based code translation.<n>We propose a novel multi-agent system TRANSAGENT, which enhances LLM-based code translation by fixing the syntax errors and semantic errors.
  arXiv  Detail & Related papers  (2024-09-30T02:53:03Z)

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.