Summary of Humanlike Cognitive Patterns As Emergent Phenomena in Large Language Models, by Zhisheng Tang et al.
Humanlike Cognitive Patterns as Emergent Phenomena in Large Language Models
by Zhisheng Tang, Mayank Kejriwal
First submitted to arxiv on: 20 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper reviews the capabilities of Large Language Models (LLMs) across three cognitive domains: decision-making biases, reasoning, and creativity. It compares LLMs’ performance to human benchmarks using established psychological tests. The findings reveal that while LLMs demonstrate some human-like biases in decision-making, they lack others, indicating partial alignment with human decision-making patterns. GPT-4 models show deliberative reasoning akin to human System-2 thinking, but smaller models fall short of human-level performance in reasoning tasks. In creativity, LLMs excel in language-based tasks but struggle with divergent thinking tasks requiring real-world context. The study suggests that LLMs have potential as collaborators, augmenting creativity in human-machine problem-solving settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are special computer programs that can understand and generate human-like text. This paper looks at what these models can do well and where they struggle. It compares the models’ abilities to how humans think and make decisions. The results show that while the models can be good at making some decisions, they’re not perfect and sometimes make mistakes that humans wouldn’t. In creative tasks like writing stories, the models are very good, but in tasks that require more complex thinking, they struggle. This paper also discusses what’s missing from these models and where we could improve them. |
Keywords
» Artificial intelligence » Alignment » Gpt
