3260 papers • 126 benchmarks • 313 datasets
Code Generation is an important field to predict explicit code or program structure from multimodal data sources such as incomplete code, programs in another programming language, natural language descriptions or execution examples. Code Generation tools can assist the development of automatic programming tools to improve programming productivity. Source: Deep Learning for Source Code Modeling and Generation Image source: Measuring Coding Challenge Competence With APPS
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Use these libraries to find code-generation models and implementations
LLaMA, a collection of foundation language models ranging from 7B to 65B parameters, is introduced and it is shown that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets.
It is shown that deep learning methods can be leveraged to train a model end-to-end to automatically reverse engineer user interfaces and generate code from a single input image with over 77% of accuracy for three different platforms.
It is found that repeated sampling from the GPT language model is a surprisingly effective strategy for producing working solutions to difficult prompts, and the potential broader impacts of deploying powerful code generation technologies, covering safety, security, and economics are discussed.
GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs, is developed, a Transformer-based model pre-trained to predict the next token in a document which exhibits human-level performance on various professional and academic benchmarks.
This work develops and releases Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters, which may be a suitable substitute for closed-source models.
StructVAE is introduced, a variational auto-encoding model for semi-supervised semantic parsing, which learns both from limited amounts of parallel data, and readily-available unlabeled NL utterances, and outperforms strong supervised models.
Comprehensive experiments show that CodeT5 significantly outperforms prior methods on understanding tasks such as code defect detection and clone detection, and generation tasks across various directions including PL-NL, NL-PL, and PL-PL.
A large and diverse parallel corpus of a hundred thousands Python functions with their documentation strings (“docstrings”) generated by scraping open source repositories on GitHub is introduced.
This work trains and releases a family of large language models up to 16.1B parameters, called CODEGEN, on natural language and programming language data, and open source the training library JAXFORMER and model checkpoints, and investigates the multi-step paradigm for program synthesis.
This dissertation presents efficient deep learning models and training paradigms to map language to general purpose source code that will enable numerous applications for non-expert users as well as developers.
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