The homogenizing effect of large language models on human expression and thought

Area of Science:

• Cognitive science
• Linguistics
• Computer science
• Psychology

Background:

• Cognitive diversity, encompassing variations in language, perspective, and reasoning, is crucial for creativity and collective intelligence.
• This diversity is deeply rooted in culture, history, and individual experiences.

Purpose of the Study:

• To investigate the potential for large language models (LLMs) to standardize language and reasoning.
• To analyze how LLMs reflect and reinforce dominant linguistic and reasoning styles while marginalizing alternative voices.

Main Methods:

• Synthesizing evidence from linguistics, psychology, cognitive science, and computer science.
• Examining the design and widespread use of LLMs.
• Analyzing how LLM training data and user reliance contribute to cognitive homogenization.

Main Results:

• LLMs tend to mirror and reinforce dominant patterns found in their training data.
• Widespread reliance on LLMs amplifies convergence, leading to a standardization of language and reasoning.
• Alternative voices and reasoning strategies risk marginalization.

Conclusions:

• The increasing integration of LLMs poses a risk of cognitive homogenization.
• This standardization threatens to flatten the cognitive landscapes essential for collective intelligence and adaptability.
• Mitigation strategies are needed to preserve cognitive diversity in the age of AI.