Human Understanding
Human understanding, a multifaceted field encompassing cognitive processes and AI model capabilities, seeks to unravel how humans and machines comprehend information. Current research focuses on improving AI's ability to understand nuanced language, visual information, and complex relationships within data, employing techniques like multimodal large language models, hypergraph attention networks, and retrieval-augmented generation. These advancements have implications for various applications, including improved medical diagnosis, enhanced human-computer interaction, and more effective scientific knowledge extraction, but challenges remain in achieving truly robust and generalizable understanding in AI.
Papers
To Supervise or Not to Supervise: Understanding and Addressing the Key Challenges of Point Cloud Transfer Learning
Souhail Hadgi, Lei Li, Maks Ovsjanikov
MESIA: Understanding and Leveraging Supplementary Nature of Method-level Comments for Automatic Comment Generation
Xinglu Pan, Chenxiao Liu, Yanzhen Zou, Tao Xie, Bing Xie
Don't Listen To Me: Understanding and Exploring Jailbreak Prompts of Large Language Models
Zhiyuan Yu, Xiaogeng Liu, Shunning Liang, Zach Cameron, Chaowei Xiao, Ning Zhang