Human Like
Research on "human-like" qualities in artificial intelligence focuses on developing models that exhibit behaviors and cognitive abilities resembling those of humans, aiming to improve AI's interaction with and understanding of humans. Current research emphasizes evaluating and enhancing aspects like reasoning, language understanding, and emotional intelligence in large language models (LLMs) and other AI architectures, often employing techniques like cognitive prompting, psychometric analysis, and imitation learning. This work is significant for advancing AI safety and trustworthiness, improving human-computer interaction, and providing novel tools for psychological and cognitive science research.
Papers
Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Shuming Shi, Zhaopeng Tu
Does Conceptual Representation Require Embodiment? Insights From Large Language Models
Qihui Xu, Yingying Peng, Samuel A. Nastase, Martin Chodorow, Minghua Wu, Ping Li
The Role of AI in Human-AI Creative Writing for Hong Kong Secondary Students
Hengky Susanto, David James Woo, Kai Guo
Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading
Shuwen Deng, David R. Reich, Paul Prasse, Patrick Haller, Tobias Scheffer, Lena A. Jäger
Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games
Stephanie Milani, Arthur Juliani, Ida Momennejad, Raluca Georgescu, Jaroslaw Rzpecki, Alison Shaw, Gavin Costello, Fei Fang, Sam Devlin, Katja Hofmann
Bipedal Robot Running: Human-like Actuation Timing Using Fast and Slow Adaptations
Yusuke Sakurai, Tomoya Kamimura, Yuki Sakamoto, Shohei Nishii, Kodai Sato, Yuta Fujiwara, Akihito Sano