Summary of Enhancing Cross-language Code Translation Via Task-specific Embedding Alignment in Retrieval-augmented Generation, by Manish Bhattarai et al.
Enhancing Cross-Language Code Translation via Task-Specific Embedding Alignment in Retrieval-Augmented Generation
by Manish Bhattarai, Minh Vu, Javier E. Santos, Ismael Boureima, Daniel O’ Malley
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel method for enhancing cross-language code translation from Fortran to C++ by integrating task-specific embedding alignment into a Retrieval-Augmented Generation (RAG) framework. The approach utilizes a dataset of 25,000 Fortran code snippets and corresponding C++ translations generated using the LLaMA 3.1-8B language model. Pairwise CodeBLEU scores are computed to capture fine-grained similarities between generated translations and ground truth examples, serving as supervision signals for optimizing the embedding model. By integrating these optimized embeddings into the RAG framework, the approach significantly enhances both retrieval accuracy and code generation quality over methods employing generic embeddings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to help computers translate code from one language (Fortran) to another (C++). The goal is to make this translation better by using special “embeddings” that are connected to the task of translating code. This approach uses a big dataset of Fortran and C++ code snippets, as well as a powerful language model called LLaMA. By comparing generated translations with real ones, the method learns to improve its code translation skills. As a result, this new way of translating code is better than previous methods that didn’t use these special embeddings. |
Keywords
» Artificial intelligence » Alignment » Embedding » Language model » Llama » Rag » Retrieval augmented generation » Translation
