Domain Knowledge
Domain knowledge integration into large language models (LLMs) is a crucial area of research aiming to enhance the accuracy, reliability, and explainability of LLMs for domain-specific tasks. Current efforts focus on incorporating domain knowledge through various methods, including knowledge graphs, ontologies, and retrieval-augmented generation (RAG), often employing architectures like mixture-of-experts models and neurosymbolic agents. This research is significant because it addresses the limitations of general-purpose LLMs in specialized fields, leading to improved performance in applications ranging from medical diagnosis to scientific discovery and financial analysis.
Papers
Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Tasks
Yuxuan Lu, Bingsheng Yao, Shao Zhang, Yun Wang, Peng Zhang, Tun Lu, Toby Jia-Jun Li, Dakuo Wang
Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations
Jing Yao, Wei Xu, Jianxun Lian, Xiting Wang, Xiaoyuan Yi, Xing Xie
Open Domain Knowledge Extraction for Knowledge Graphs
Kun Qian, Anton Belyi, Fei Wu, Samira Khorshidi, Azadeh Nikfarjam, Rahul Khot, Yisi Sang, Katherine Luna, Xianqi Chu, Eric Choi, Yash Govind, Chloe Seivwright, Yiwen Sun, Ahmed Fakhry, Theo Rekatsinas, Ihab Ilyas, Xiaoguang Qi, Yunyao Li
Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design
Markus J. Buehler