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
Applying Machine Learning to Life Insurance: some knowledge sharing to master it
Antoine Chancel, Laura Bradier, Antoine Ly, Razvan Ionescu, Laurene Martin, Marguerite Sauce
Distilling the Knowledge of BERT for CTC-based ASR
Hayato Futami, Hirofumi Inaguma, Masato Mimura, Shinsuke Sakai, Tatsuya Kawahara