Knowledge Based
Knowledge-based systems research focuses on effectively integrating and utilizing knowledge within artificial intelligence, primarily aiming to improve the accuracy, reliability, and interpretability of AI models. Current research emphasizes enhancing large language models (LLMs) with external knowledge graphs, employing techniques like retrieval-augmented generation and knowledge distillation to overcome limitations such as hallucinations and catastrophic forgetting. This work is significant because it addresses critical challenges in AI, leading to more robust and trustworthy systems with applications in diverse fields like education, healthcare, and materials science.
Papers
Two Heads Are Better Than One: Integrating Knowledge from Knowledge Graphs and Large Language Models for Entity Alignment
Linyao Yang, Hongyang Chen, Xiao Wang, Jing Yang, Fei-Yue Wang, Han Liu
NanoNER: Named Entity Recognition for nanobiology using experts' knowledge and distant supervision
Martin Lentschat, Cyril Labbé, Ran Cheng