Code translation tools are developed for automatic source-to-source
translation. Although learning-based transpilers have shown impressive
enhancement against rule-based counterparts, owing to their task-specific
pre-training on extensive monolingual corpora. Their current performance still
remains unsatisfactory for practical deployment, and the associated training
resources are also prohibitively expensive. LLMs pre-trained on huge amounts of
human-written code/text have shown remarkable performance in many code
intelligence tasks due to their powerful generality, even without task-specific
training. Thus, LLMs can potentially circumvent the above limitations, but they
have not been exhaustively explored yet. This paper investigates diverse LLMs
and learning-based transpilers for automated code translation tasks, finding
that: although certain LLMs have outperformed current transpilers, they still
have some accuracy issues, where most of the failures are induced by a lack of
comprehension of source programs (38.51%), missing clear instructions on I/O
types in translation (14.94%), and ignoring discrepancies between source and
target programs (41.38%). Enlightened by the above findings, we propose
UniTrans, an Unified code Translation framework, applicable to various LLMs,
for unleashing their power in this field. Specifically, UniTrans first craft a
series of test cases for target programs with the assistance of source
programs. Next, it harnesses the above auto-generated test cases to augment the
code translation and then evaluate their correctness via execution. Afterward,
UniTrans further (iteratively) repairs incorrectly translated programs prompted
by test case execution results. Extensive experiments are conducted on six
translation datasets between Python, Java, and C++. Three recent LLMs of
diverse sizes are tested with UniTrans, and all achieve substantial
improvements.

LLMs 和基于学习的转译器在自动代码转换任务中取得了显著的性能提升，但当前仍存在一些准确性问题和资源限制。本研究发现，大部分失败源于对源程序的理解不足、在转换中缺少清晰的 I/O 类型指令以及源程序与目标程序之间的差异被忽略。鉴于以上研究结果，我们提出了 UniTrans，一个适用于多种 LLMs 的统一代码转换框架，通过生成测试用例、自动增强转换并验证正确性的执行过程，进一步修复转换错误。在 Python、Java 和 C++ 之间的六个转换数据集上进行了大量实验，三个最近的 LLMs 通过 UniTrans 实现了大幅度的改进。

探索和释放大型语言模型在自动代码翻译中的潜力

Exploring and Unleashing the Power of Large Language Models in Automated  Code Translation

Prior works in cross-lingual named entity recognition (NER) with no/little
labeled data fall into two primary categories: model transfer based and data
transfer based methods. In this paper we find that both method types can
complement each other, in the sense that, the former can exploit context
information via language-independent features but sees no task-specific
information in the target language; while the latter generally generates pseudo
target-language training data via translation but its exploitation of context
information is weakened by inaccurate translations. Moreover, prior works
rarely leverage unlabeled data in the target language, which can be
effortlessly collected and potentially contains valuable information for
improved results. To handle both problems, we propose a novel approach termed
UniTrans to Unify both model and data Transfer for cross-lingual NER, and
furthermore, to leverage the available information from unlabeled
target-language data via enhanced knowledge distillation. We evaluate our
proposed UniTrans over 4 target languages on benchmark datasets. Our
experimental results show that it substantially outperforms the existing
state-of-the-art methods.

本文提出了一种称为 UniTrans 的新方法，以统一模型和数据转移，通过增强的知识蒸馏从未标记的目标语言数据中利用可用信息，对比 4 种目标语言的基准数据集表明，UniTrans 显著优于现有的最先进方法。