Real Human
Research on "Real Human" focuses on understanding and replicating human capabilities, particularly in perception, cognition, and social interaction, using artificial intelligence models. Current efforts concentrate on developing and evaluating large language models (LLMs) and large vision-language models (LVLMs), often incorporating architectures like transformers and diffusion models, to benchmark AI performance against human benchmarks in tasks ranging from visual perception and emotion recognition to complex decision-making and social interaction. These studies aim to improve AI systems' alignment with human behavior and understanding, ultimately impacting fields like human-computer interaction, robotics, and social sciences.
Papers
Overinformative Question Answering by Humans and Machines
Polina Tsvilodub, Michael Franke, Robert D. Hawkins, Noah D. Goodman
Are Machine Rationales (Not) Useful to Humans? Measuring and Improving Human Utility of Free-Text Rationales
Brihi Joshi, Ziyi Liu, Sahana Ramnath, Aaron Chan, Zhewei Tong, Shaoliang Nie, Qifan Wang, Yejin Choi, Xiang Ren
HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion
Mustafa Işık, Martin Rünz, Markos Georgopoulos, Taras Khakhulin, Jonathan Starck, Lourdes Agapito, Matthias Nießner
Humans are Still Better than ChatGPT: Case of the IEEEXtreme Competition
Anis Koubaa, Basit Qureshi, Adel Ammar, Zahid Khan, Wadii Boulila, Lahouari Ghouti
MultiModal-GPT: A Vision and Language Model for Dialogue with Humans
Tao Gong, Chengqi Lyu, Shilong Zhang, Yudong Wang, Miao Zheng, Qian Zhao, Kuikun Liu, Wenwei Zhang, Ping Luo, Kai Chen
Do Large Language Models Show Decision Heuristics Similar to Humans? A Case Study Using GPT-3.5
Gaurav Suri, Lily R. Slater, Ali Ziaee, Morgan Nguyen