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
Domain Mastery Benchmark: An Ever-Updating Benchmark for Evaluating Holistic Domain Knowledge of Large Language Model--A Preliminary Release
Zhouhong Gu, Xiaoxuan Zhu, Haoning Ye, Lin Zhang, Zhuozhi Xiong, Zihan Li, Qianyu He, Sihang Jiang, Hongwei Feng, Yanghua Xiao
System III: Learning with Domain Knowledge for Safety Constraints
Fazl Barez, Hosien Hasanbieg, Alesandro Abbate
Distribution-free Deviation Bounds and The Role of Domain Knowledge in Learning via Model Selection with Cross-validation Risk Estimation
Diego Marcondes, Cláudia Peixoto
Interpretability from a new lens: Integrating Stratification and Domain knowledge for Biomedical Applications
Anthony Onoja, Francesco Raimondi