Non‐approximation‐based outputs stabilization for switched large‐scale nonlinear systems with quantized inputs and sensor uncertainties

Wang, Wanli; Lin, Yan

doi:10.1002/asjc.3042

Cited by 2 publications

(1 citation statement)

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“…Future studies should adopt broader scope assessments, akin to ToM tests, while avoiding their lack of discriminative power. Besides, with recent advancements, LLMs are now capable of processing multimodal information (Gao et al, 2023; Wang et al, 2023; Yang et al, 2023; Zhu et al, 2023). Therefore, future studies should investigate how LLMs interpret complex emotions from multimodal inputs, such as text combined with facial expressions or the tone of voice.…”

Section: Discussionmentioning

confidence: 99%

Emotional intelligence of Large Language Models

Wang,

Li,

Yin

et al. 2023

Journal of Pacific Rim Psychology

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Large Language Models (LLMs) have demonstrated remarkable abilities across numerous disciplines, primarily assessed through tasks in language generation, knowledge utilization, and complex reasoning. However, their alignment with human emotions and values, which is critical for real-world applications, has not been systematically evaluated. Here, we assessed LLMs' Emotional Intelligence (EI), encompassing emotion recognition, interpretation, and understanding, which is necessary for effective communication and social interactions. Specifically, we first developed a novel psychometric assessment focusing on Emotion Understanding (EU), a core component of EI. This test is an objective, performance-driven, and text-based evaluation, which requires evaluating complex emotions in realistic scenarios, providing a consistent assessment for both human and LLM capabilities. With a reference frame constructed from over 500 adults, we tested a variety of mainstream LLMs. Most achieved above-average Emotional Quotient (EQ) scores, with GPT-4 exceeding 89% of human participants with an EQ of 117. Interestingly, a multivariate pattern analysis revealed that some LLMs apparently did not rely on the human-like mechanism to achieve human-level performance, as their representational patterns were qualitatively distinct from humans. In addition, we discussed the impact of factors such as model size, training method, and architecture on LLMs' EQ. In summary, our study presents one of the first psychometric evaluations of the human-like characteristics of LLMs, which may shed light on the future development of LLMs aiming for both high intellectual and emotional intelligence. Project website: https://emotional-intelligence.github.io/

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Section: Discussionmentioning

confidence: 99%