Summary of Enhancing Code Translation in Language Models with Few-shot Learning Via Retrieval-augmented Generation, by Manish Bhattarai et al.
Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation
by Manish Bhattarai, Javier E. Santos, Shawn Jones, Ayan Biswas, Boian Alexandrov, Daniel O’Malley
First submitted to arxiv on: 29 Jul 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 novel approach introduced in this paper enhances code translation through Few-Shot Learning, combining Retrieval-Augmented Generation (RAG) with retrieval-based techniques. This method leverages a repository of existing code translations to dynamically retrieve relevant examples, guiding the model in translating new code segments. By utilizing RAG instead of traditional fine-tuning methods, the approach can adapt to diverse translation tasks without extensive retraining. The paper demonstrates the superiority of this approach over traditional zero-shot methods on various datasets, including Starcoder, Llama3-70B Instruct, and GPT-3.5 Turbo, especially when translating between Fortran and CPP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us write better code by using big language models (LLMs) to translate programming languages. But these models often struggle with complex translations because they don’t understand the context well enough. To solve this problem, the authors came up with a new way of training LLMs that uses existing examples of translated code. This method is called Retrieval-Augmented Generation (RAG). The authors tested their approach on many different datasets and found that it works much better than traditional methods. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Gpt » Rag » Retrieval augmented generation » Translation » Zero shot
