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Friday, April 12, 2024

Large language models show human-like content biases in transmission chain experiments

Acerbi, A., & Stubbersfield, J. M. (2023).
PNAS, 120(44), e2313790120.


As the use of large language models (LLMs) grows, it is important to examine whether they exhibit biases in their output. Research in cultural evolution, using transmission chain experiments, demonstrates that humans have biases to attend to, remember, and transmit some types of content over others. Here, in five preregistered experiments using material from previous studies with human participants, we use the same, transmission chain-like methodology, and find that the LLM ChatGPT-3 shows biases analogous to humans for content that is gender-stereotype-consistent, social, negative, threat-related, and biologically counterintuitive, over other content. The presence of these biases in LLM output suggests that such content is widespread in its training data and could have consequential downstream effects, by magnifying preexisting human tendencies for cognitively appealing and not necessarily informative, or valuable, content.


Use of AI in the production of text through Large Language Models (LLMs) is widespread and growing, with potential applications in journalism, copywriting, academia, and other writing tasks. As such, it is important to understand whether text produced or summarized by LLMs exhibits biases. The studies presented here demonstrate that the LLM ChatGPT-3 reflects human biases for certain types of content in its production. The presence of these biases in LLM output has implications for its common use, as it may magnify human tendencies for content which appeals to these biases.

Here are the main points:
  • LLMs display stereotype-consistent biases, just like humans: Similar to people, LLMs were more likely to preserve information confirming stereotypes over information contradicting them.
  • Bias location might differ: Unlike humans, whose biases can shift throughout the retelling process, LLMs primarily showed bias in the first retelling. This suggests their biases stem from their training data rather than a complex cognitive process.
  • Simple summarization may suffice: The first retelling step caused the most content change, implying that even a single summarization by an LLM can reveal its biases. This simplifies the research needed to detect and analyze LLM bias.
  • Prompting for different viewpoints could reduce bias: The study suggests experimenting with different prompts to encourage LLMs to consider broader perspectives and potentially mitigate inherent biases.