Abstract
Knowledge-Enhanced Large Language Models (K-LLMs) system enhances Large Language Models (LLMs) abilities using external knowledge. Existing K-LLMs toolkits mainly focus on free-textual knowledge, lacking support for heterogeneous knowledge like tables and knowledge graphs, and fall short in comprehensive datasets, models, and user-friendly experience. To address this gap, we introduce KMatrix: a flexible heterogeneous knowledge enhancement toolkit for LLMs including verbalizing-retrieval and parsing-query methods. Our modularity and control-logic flow diagram design flexibly supports the entire lifecycle of various complex K-LLMs systems, including training, evaluation, and deployment. To assist K-LLMs system research, a series of related knowledge, datasets, and models are integrated into our toolkit, along with performance analyses of K-LLMs systems enhanced by different types of knowledge. Using our toolkit, developers can rapidly build, evaluate, and deploy their own K-LLMs systems.- Anthology ID:
- 2024.emnlp-demo.29
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Delia Irazu Hernandez Farias, Tom Hope, Manling Li
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 280–290
- Language:
- URL:
- https://aclanthology.org/2024.emnlp-demo.29
- DOI:
- 10.18653/v1/2024.emnlp-demo.29
- Cite (ACL):
- Shun Wu, Di Wu, Kun Luo, XueYou Zhang, Jun Zhao, and Kang Liu. 2024. KMatrix: A Flexible Heterogeneous Knowledge Enhancement Toolkit for Large Language Model. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 280–290, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- KMatrix: A Flexible Heterogeneous Knowledge Enhancement Toolkit for Large Language Model (Wu et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.emnlp-demo.29.pdf