Full-Text Level Knowledge Updating System

✍️ Online Demo • 🤗 HF Repo • 📃 Paper • 📎 Presentation • 🗒️ Master's Thesis

Overview

Event Triggered Article Updating System is a long article updating application for knowledge update.

Spearheaded the development of an Event Triggered Article Updating System, a cutting-edge application designed for updating long articles with new knowledge. This project showcases a significant advancement in handling full-text knowledge updating triggered by news events, leveraging the capabilities of large language models (LLMs).

The whole system is trained on NetKu dataset for knowledge updating in full-text triggered by a News Event.

Demo

A live demonstation of the model can be accessed at Live Demo with GPU support, and HF Space with CPU support.

Key Features

1. Long texts input support: Overcame the limitations of existing LLMs by enabling the system to understand and process long context inputs. Developed a unique approach allowing for unlimited full-article input lengths, with each paragraph handling up to 4,096 tokens.

2. Instruction-Tuned Models: Implemented multiple baseline models, including those fine-tuned on LLaMA, Alpaca, Vicuna, and GPT-based models, demonstrating versatility and adaptability in model training.

3. Innovative Model Architecture: Proposed and developed a new Encoder-Decoder based model architecture. Conducted comprehensive evaluations to prove the effectiveness of this novel approach in the context of knowledge updating.

Citations

If you use our code, data, or models in your research, please cite this repository. You can use the following BibTeX entry:

@inproceedings{lee2022multi,
  title={A Multi-grained Dataset for News Event Triggered Knowledge Update},
  author={Lee, Yu-Ting and Tang, Ying-Jhe and Cheng, Yu-Chung and Chen, Pai-Lin and Li, Tsai-Yen and Huang, Hen-Hsen},
  booktitle={Proceedings of the 31st ACM International Conference on Information \& Knowledge Management},
  pages={4158--4162},
  year={2022}
}

License

The code in this project is licensed under the Apache 2.0 License - see the LICENSE file for details.

OpenAI Data Acknowledgment

The text generation included in this project were generated using OpenAI's models and are subject to OpenAI's Terms of Use. Please review OpenAI's Terms of Use for details on usage and limitations.

Acknowledgements

This work is supported by

1. National Science and Technology Council, Taiwan, under grants 109-2222-E-001-004-MY3 and 109-2628-H-004-001-MY4.
2. Institute of Information Science, Academia Sinica, Taiwan.
3. National Chengchi University, Taiwan.
4. We thank Meta LLaMA team, Vicuna team, Lightning AI and ISI-NLP for their contributions.