Humanlike Cognitive Patterns as Emergent Phenomena in Large Language Models

• URL: http://arxiv.org/abs/2412.15501v1
• Date: Fri, 20 Dec 2024 02:26:56 GMT
• Title: Humanlike Cognitive Patterns as Emergent Phenomena in Large Language Models
• Authors: Zhisheng Tang, Mayank Kejriwal,
• Abstract summary: We systematically review Large Language Models' capabilities across three important cognitive domains: decision-making biases, reasoning, and creativity.<n>On decision-making, our synthesis reveals that while LLMs demonstrate several human-like biases, some biases observed in humans are absent.<n>On reasoning, advanced LLMs like GPT-4 exhibit deliberative reasoning akin to human System-2 thinking, while smaller models fall short of human-level performance.<n>A distinct dichotomy emerges in creativity: while LLMs excel in language-based creative tasks, such as storytelling, they struggle with divergent thinking tasks that require real-world context.
• Score: 2.9312156642007294
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Research on emergent patterns in Large Language Models (LLMs) has gained significant traction in both psychology and artificial intelligence, motivating the need for a comprehensive review that offers a synthesis of this complex landscape. In this article, we systematically review LLMs' capabilities across three important cognitive domains: decision-making biases, reasoning, and creativity. We use empirical studies drawing on established psychological tests and compare LLMs' performance to human benchmarks. On decision-making, our synthesis reveals that while LLMs demonstrate several human-like biases, some biases observed in humans are absent, indicating cognitive patterns that only partially align with human decision-making. On reasoning, advanced LLMs like GPT-4 exhibit deliberative reasoning akin to human System-2 thinking, while smaller models fall short of human-level performance. A distinct dichotomy emerges in creativity: while LLMs excel in language-based creative tasks, such as storytelling, they struggle with divergent thinking tasks that require real-world context. Nonetheless, studies suggest that LLMs hold considerable potential as collaborators, augmenting creativity in human-machine problem-solving settings. Discussing key limitations, we also offer guidance for future research in areas such as memory, attention, and open-source model development.

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