3 citations · 7 across the 6 of their papers we have counts for
6 papers
Superminds Test: Actively Evaluating Collective Intelligence of Agent Society via Probing Agents
Xirui Li, Ming Li, Yunze Xiao +4
Collective intelligence refers to the ability of a group to achieve outcomes beyond what any individual member can accomplish alone. As large language model agents scale to populat…
Large Language Models Are Human-Like Internally
Tatsuki Kuribayashi, Yohei Oseki, Souhaib Ben Taieb +2
Recent cognitive modeling studies have reported that larger language models (LMs) exhibit a poorer fit to human reading behavior (Oh and Schuler, 2023b; Shain et al., 2024; Kuribay…
Libra-Leaderboard: Towards Responsible AI through a Balanced Leaderboard of Safety and Capability
Haonan Li, Xudong Han, Zenan Zhai +32
To address this gap, we introduce Libra-Leaderboard, a comprehensive framework designed to rank LLMs through a balanced evaluation of performance and safety. Combining a dynamic le…
Emergent Word Order Universals from Cognitively-Motivated Language Models
Tatsuki Kuribayashi, Ryo Ueda, Ryo Yoshida +3
The world's languages exhibit certain so-called typological or implicational universals; for example, Subject-Object-Verb (SOV) languages typically use postpositions. Explaining th…
Psychometric Predictive Power of Large Language Models
Tatsuki Kuribayashi, Yohei Oseki, Timothy Baldwin
Instruction tuning aligns the response of large language models (LLMs) with human preferences. Despite such efforts in human--LLM alignment, we find that instruction tuning does no…
Does Vision Accelerate Hierarchical Generalization in Neural Language Learners?
Tatsuki Kuribayashi, Timothy Baldwin
Neural language models (LMs) are arguably less data-efficient than humans from a language acquisition perspective. One fundamental question is why this human-LM gap arises. This st…