Collaboration is all you need: LLM Assisted Safe Code Translation
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918107948253184 |
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| author | Karanjai, Rabimba Blackshear, Sam Xu, Lei Shi, Weidong |
| author_facet | Karanjai, Rabimba Blackshear, Sam Xu, Lei Shi, Weidong |
| contents | This paper introduces UniTranslator, a visionary framework that re-imagines code translation as a collaborative endeavor among multiple, compact LLMs. By orchestrating the interaction of specialized agents, each focused on different aspects of the translation process and grounded in a deep understanding of programming concepts, UniTranslator achieves a level of accuracy and efficiency that rivals larger, monolithic models. Our preliminary evaluation demonstrates the potential of UniTranslator to overcome the limitations of existing approaches and unlock the power of smaller LLMs for complex code translation tasks. We explore the effectiveness of this dynamic multi-agent paradigm in handling diverse language pairs, including low-resource languages, and in mitigating common issues such as code artifacts and hallucinations through the use of Natural Language Inference (NLI) grounding and iterative feedback mechanisms |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11237 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Collaboration is all you need: LLM Assisted Safe Code Translation Karanjai, Rabimba Blackshear, Sam Xu, Lei Shi, Weidong Artificial Intelligence Computation and Language Software Engineering This paper introduces UniTranslator, a visionary framework that re-imagines code translation as a collaborative endeavor among multiple, compact LLMs. By orchestrating the interaction of specialized agents, each focused on different aspects of the translation process and grounded in a deep understanding of programming concepts, UniTranslator achieves a level of accuracy and efficiency that rivals larger, monolithic models. Our preliminary evaluation demonstrates the potential of UniTranslator to overcome the limitations of existing approaches and unlock the power of smaller LLMs for complex code translation tasks. We explore the effectiveness of this dynamic multi-agent paradigm in handling diverse language pairs, including low-resource languages, and in mitigating common issues such as code artifacts and hallucinations through the use of Natural Language Inference (NLI) grounding and iterative feedback mechanisms |
| title | Collaboration is all you need: LLM Assisted Safe Code Translation |
| topic | Artificial Intelligence Computation and Language Software Engineering |
| url | https://arxiv.org/abs/2503.11237 |