Enhancing LLM-Based Code Translation with Verified Multi-Semantic Representations

• URL: http://arxiv.org/abs/2606.11863v1
• Date: Wed, 10 Jun 2026 09:38:40 GMT
• Title: Enhancing LLM-Based Code Translation with Verified Multi-Semantic Representations
• Abstract summary: Multisage is a multi-semantic augmentation and self-calibration framework for code translation.<n>It generates diverse augmented semantics, including code summaries, function-level test cases, and API-oriented descriptions and tests.<n>Experiments on the HumanEval-X code translation benchmark show that Multisage improves translation success rates by up to 2.22 times across diverse backbone models.
• Score: 12.071793069280623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have shown great promise for automated code translation, yet existing approaches often rely on token-level statistical patterns rather than sufficient understanding of program semantics. As a result, translated programs may still contain logical and semantic errors. Although high-quality semantic guidance, such as functional descriptions and test cases, can help mitigate these errors, such resources are often unavailable in real-world scenarios. This raises two key challenges: how to construct rich semantic information directly from source code, and how to ensure that such semantics are accurate and reliable enough to guide translation.To address these challenges, we propose Multisage, a multi-semantic augmentation and self-calibration framework for LLM-based code translation. Multisage consists of three modules. First, a semantic representation parsing module extracts structured base semantics from source code, including data-flow graphs, type constraints, and external API information. Second, a multi-semantic augmentation module builds on these representations to generate diverse augmented semantics, including code summaries, function-level test cases, and API-oriented descriptions and tests. Third, a semantic consistency calibration module uses semantics-preserving mutations and cross-semantic consistency verification to filter, calibrate, and refine the generated semantics.Experiments on the HumanEval-X code translation benchmark show that Multisage improves translation success rates by up to 2.22 times across diverse backbone models. It consistently outperforms vanilla prompting, instruction-tuned LLMs, and Chain-of-Thought reasoning, with the largest gains observed on smaller models. These results demonstrate that explicit semantic augmentation can substantially improve the reliability of LLM-based code translation.

Related papers

• $\mathcal{S}^2$IT: Stepwise Syntax Integration Tuning for Large Language Models in Aspect Sentiment Quad Prediction [51.165432266846096]
  syntactic structure information is underutilized in the generative paradigm of large language models (LLMs)<n>S2IT progressively integrates syntactic structure knowledge into LLMs through a multi-step tuning process.<n> Experiments demonstrate that S2IT significantly improves state-of-the-art performance across multiple datasets.
  arXiv  Detail & Related papers  (2026-04-25T13:24:29Z)
• DiffuRank: Effective Document Reranking with Diffusion Language Models [71.16830004674513]
  We propose DiffuRank, a reranking framework built upon diffusion language models (dLLMs)<n>dLLMs support more flexible decoding and generation processes that are not constrained to a left-to-right order.<n>We show dLLMs achieve performance comparable to, and in some cases exceeding, that of autoregressive LLMs with similar model sizes.
  arXiv  Detail & Related papers  (2026-02-13T02:18:14Z)
• Readability-Robust Code Summarization via Meta Curriculum Learning [53.44612630063336]
  In the real world, code is often poorly structured or obfuscated, significantly degrading model performance.<n>We propose RoFTCodeSum, a novel fine-tuning method that enhances the robustness of code summarization against poorly readable code.
  arXiv  Detail & Related papers  (2026-01-09T02:38:24Z)
• TIT: A Tree-Structured Instruction Tuning Approach for LLM-Based Code Translation [11.882496324328905]
  We propose TIT, a Tree-structured Instruction Tuning paradigm for LLM-based code translation.<n>To mitigate syntactic confusion, the syntactic information representation module integrates language-agnostic syntactic features.<n>To generate high-quality fine-grained parallel data, the fine-grained parallel dataset augmentation module aligns nodes with code segments.
  arXiv  Detail & Related papers  (2025-10-10T13:53:46Z)
• PLSemanticsBench: Large Language Models As Programming Language Interpreters [31.611330217819713]
  As large language models (LLMs) excel at code reasoning, a natural question arises: can an LLM execute programs (i.e., act as an interpreter) purely based on a programming language's formal semantics?<n>We study this question using the imperative language IMP, formalized via small-step operational semantics (SOS) and rewriting-based operational semantics (K-semantics)<n>We introduce three evaluation sets-Human-Written, LLM-Translated, and Fuzzer- Generated-whose difficulty is controlled by code-complexity metrics spanning the size, control-flow, and data-flow axes.
  arXiv  Detail & Related papers  (2025-10-03T18:23:26Z)
• Function-to-Style Guidance of LLMs for Code Translation [59.487054943812836]
  We propose F2STrans, a function-to-style guiding paradigm designed to improve the performance of large language models in code translation.<n>Our approach comprises two key stages: (1) Functional learning, which optimize translation correctness using high-quality source-target code pairs.<n>We introduce a novel code translation benchmark that includes up-to-date source code, extensive test cases, and manually annotated ground-truth translations.
  arXiv  Detail & Related papers  (2025-07-15T08:25:02Z)
• 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)
• A test-free semantic mistakes localization framework in Neural Code Translation [32.5036379897325]
  We present EISP, a static analysis framework based on the Large Language Model (LLM) The framework generates a semantic mapping between source code and translated code. EISP connects each pair of sub-code fragments with fine-grained knowledge hints through an AI chain.
  arXiv  Detail & Related papers  (2024-10-30T08:53:33Z)
• EMMA: Efficient Visual Alignment in Multi-Modal LLMs [56.03417732498859]
  EMMA is a lightweight cross-modality module designed to efficiently fuse visual and textual encodings.<n>EMMA boosts performance across multiple tasks by up to 9.3% while significantly improving robustness against hallucinations.
  arXiv  Detail & Related papers  (2024-10-02T23:00:31Z)
• An Empirical Study on Capability of Large Language Models in Understanding Code Semantics [4.638578225024275]
  Large Language Models for Code (code LLMs) have demonstrated remarkable performance across various software engineering (SE) tasks. This paper introduces EMPICA, a framework designed to evaluate the capabilities of code LLMs in understanding code semantics.
  arXiv  Detail & Related papers  (2024-07-04T03:40:58Z)
• M2CVD: Enhancing Vulnerability Semantic through Multi-Model Collaboration for Code Vulnerability Detection [52.4455893010468]
  Large Language Models (LLMs) have strong capabilities in code comprehension, but fine-tuning costs and semantic alignment issues limit their project-specific optimization. Code models such CodeBERT are easy to fine-tune, but it is often difficult to learn vulnerability semantics from complex code languages. This paper introduces the Multi-Model Collaborative Vulnerability Detection approach (M2CVD) to improve the detection accuracy of code models.
  arXiv  Detail & Related papers  (2024-06-10T00:05:49Z)
• Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation [22.219645213202178]
  This paper proposes the "Semantic Chain-of-Thought" approach to intruduce semantic information of code, named SeCoT. We show that SeCoT can achieves state-of-the-art performance, greatly improving the potential for large models and code generation.
  arXiv  Detail & Related papers  (2023-10-16T05:09:58Z)

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.