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
Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation
Guanting Dong, Yutao Zhu, Chenghao Zhang, Zechen Wang, Zhicheng Dou, Ji-Rong Wen
PianoBART: Symbolic Piano Music Generation and Understanding with Large-Scale Pre-Training
Xiao Liang, Zijian Zhao, Weichao Zeng, Yutong He, Fupeng He, Yiyi Wang, Chengying Gao
Predicting the Understandability of Computational Notebooks through Code Metrics Analysis
Mojtaba Mostafavi Ghahfarokhi, Alireza Asadi, Arash Asgari, Bardia Mohammadi, Masih Beigi Rizi, Abbas Heydarnoori
Understanding Understanding: A Pragmatic Framework Motivated by Large Language Models
Kevin Leyton-Brown, Yoav Shoham