Studies of koala behavioural ecology and implications for koala conservation

Jiang, Alex

doi:10.14264/be4e6b1

Cited by 1 publication

(1 citation statement)

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“…Finally, while the factors examined in this study contribute to our standing of LLM's EU, they are largely descriptive and thus do not establish causal relationships. With the recent progress of open-source LLMs (Bai et al, 2022; Chiang et al, 2023; Conover et al, 2023; Geng et al, 2023; Köpf et al, 2023; Peng et al, 2023; Taori et al, 2023b; Touvron et al, 2023; Zeng et al, 2022; Zheng et al, 2023), direct manipulation of the potentially influential factors, such as training approaches and model size, has become plausible. Such manipulations will facilitate the establishment of causal relationships between these factors and models' EI ability, offering valuable insights for the development of future LLMs with better EI.…”

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%