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
Quantifying Human Bias and Knowledge to guide ML models during Training
Hrishikesh Viswanath, Andrey Shor, Yoshimasa Kitaguchi
A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset
Jiaxin Deng, Dong Shen, Haojie Pan, Xiangyu Wu, Ximan Liu, Gaofeng Meng, Fan Yang, Size Li, Ruiji Fu, Zhongyuan Wang