Assessing the nature of large language models: A caution against anthropocentrism
arXiv:2309.07683
Abstract
Generative AI models garnered a large amount of public attention and speculation with the release of OpenAIs chatbot, ChatGPT. At least two opinion camps exist: one excited about possibilities these models offer for fundamental changes to human tasks, and another highly concerned about power these models seem to have. To address these concerns, we assessed several LLMs, primarily GPT 3.5, using standard, normed, and validated cognitive and personality measures. For this seedling project, we developed a battery of tests that allowed us to estimate the boundaries of some of these models capabilities, how stable those capabilities are over a short period of time, and how they compare to humans. Our results indicate that LLMs are unlikely to have developed sentience, although its ability to respond to personality inventories is interesting. GPT3.5 did display large variability in both cognitive and personality measures over repeated observations, which is not expected if it had a human-like personality. Variability notwithstanding, LLMs display what in a human would be considered poor mental health, including low self-esteem, marked dissociation from reality, and in some cases narcissism and psychopathy, despite upbeat and helpful responses.
31 pages, 6 figures
References in corpus (13)
- Sparks of Artificial General Intelligence: Early experiments with GPT-4
- Emergent Abilities of Large Language Models
- The Debate Over Understanding in AI's Large Language Models
- Augmented Language Models: a Survey
- Could a Large Language Model be Conscious?
- Dissociating language and thought in large language models
- Machine Psychology
- SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks
- Evaluating Psychological Safety of Large Language Models
- Inverse Scaling: When Bigger Isn't Better
- Revisiting the Reliability of Psychological Scales on Large Language Models
- Pushing the Limits of ChatGPT on NLP Tasks
- Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning