5 citations · 23 across the 17 of their papers we have counts for
4 papers · 1 filter
CharacterBench: Benchmarking Character Customization of Large Language Models
Jinfeng Zhou, Yongkang Huang, Bosi Wen +13
Character-based dialogue (aka role-playing) enables users to freely customize characters for interaction, which often relies on LLMs, raising the need to evaluate LLMs' character c…
Benchmarking Complex Instruction-Following with Multiple Constraints Composition
Bosi Wen, Pei Ke, Xiaotao Gu +11
Instruction following is one of the fundamental capabilities of large language models (LLMs). As the ability of LLMs is constantly improving, they have been increasingly applied to…
ToMBench: Benchmarking Theory of Mind in Large Language Models
Zhuang Chen, Jincenzi Wu, Jinfeng Zhou +8
Theory of Mind (ToM) is the cognitive capability to perceive and ascribe mental states to oneself and others. Recent research has sparked a debate over whether large language model…
EmoBench: Evaluating the Emotional Intelligence of Large Language Models
Sahand Sabour, Siyang Liu, Zheyuan Zhang +7
Recent advances in Large Language Models (LLMs) have highlighted the need for robust, comprehensive, and challenging benchmarks. Yet, research on evaluating their Emotional Intelli…