Summary of A Comparative Study on Code Generation with Transformers, by Namrata Das et al.
A Comparative Study on Code Generation with Transformers
by Namrata Das, Rakshya Panta, Neelam Karki, Ruchi Manandhar, Dinesh Baniya Kshatri
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed “A Comparative Study on Code Generation with Transformers” paper introduces a Transformer-based model that uses NLP methodologies to automatically generate C++ source code for various problem types. The study aims to evaluate the robustness of transformer-based models by comparing their architecture complexities and capabilities in handling diverse problems, ranging from basic arithmetic to complex computations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how machines can automatically write code without human help. Scientists are trying to make computers generate code like humans do. They want to compare different ways that computer models called transformers work at generating code for different kinds of math problems. The goal is to find the best way for these models to handle easy and hard math problems. |
Keywords
» Artificial intelligence » Nlp » Transformer
