ProphetNet is a research project focused on natural language generation (NLG), offering official implementations of advanced pre-trained models and benchmarks. It targets researchers and developers in NLP seeking state-of-the-art techniques for text generation tasks. The project provides a suite of models and frameworks, including ProphetNet, GLGE, JGR, GENIE, AR-diffusion, and CRITIC, enabling exploration of various NLG paradigms.

How It Works

The project encompasses several distinct NLG approaches. ProphetNet utilizes a novel "future n-gram prediction" mechanism to enhance pre-training, aiming to improve coherence and relevance in generated text. GLGE provides baseline models for NLG benchmarks, facilitating comparative analysis. JGR explores joint learning of generation and ranking components. GENIE and AR-diffusion introduce diffusion models for text generation, leveraging continuous denoising and autoregressive properties. CRITIC focuses on LLM self-correction via external tool interaction.

Quick Start & Requirements

Installation typically involves cloning the repository and installing dependencies via pip. Specific models may require PyTorch, Transformers, and potentially CUDA-enabled GPUs for efficient training and inference. Detailed setup instructions and model-specific requirements are available within the respective sub-directories.

Highlighted Details

• Official implementations of ProphetNet, a pre-trained NLG model with future n-gram prediction.
• Includes GLGE, a benchmark for natural language generation.
• Features JGR for joint generator-ranker learning.
• Introduces GENIE and AR-diffusion, diffusion models for text generation.
• CRITIC enables LLMs to validate and rectify outputs using external tools.

Maintenance & Community

This project originates from the MSRA NLC team at Microsoft. Further community engagement and updates can be found via Microsoft's open-source channels.

Licensing & Compatibility

The repository is released under the MIT License, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

As a research project, some components may be experimental or under active development, potentially leading to breaking changes or incomplete documentation for specific models. The primary focus is on research contributions rather than production-ready deployment.