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
ChatCollab: Exploring Collaboration Between Humans and AI Agents in Software Teams
Benjamin Klieger, Charis Charitsis, Miroslav Suzara, Sierra Wang, Nick Haber, John C. Mitchell
A Comprehensive Evaluation of Semantic Relation Knowledge of Pretrained Language Models and Humans
Zhihan Cao, Hiroaki Yamada, Simone Teufel, Takenobu Tokunaga