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
Distilling Named Entity Recognition Models for Endangered Species from Large Language Models
Jesse Atuhurra, Seiveright Cargill Dujohn, Hidetaka Kamigaito, Hiroyuki Shindo, Taro Watanabe
Structural Positional Encoding for knowledge integration in transformer-based medical process monitoring
Christopher Irwin, Marco Dossena, Giorgio Leonardi, Stefania Montani
The Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey
Taojie Kuang, Pengfei Liu, Zhixiang Ren
Using Large Language Models to Automate and Expedite Reinforcement Learning with Reward Machine
Shayan Meshkat Alsadat, Jean-Raphael Gaglione, Daniel Neider, Ufuk Topcu, Zhe Xu