Apr 13, 2026 11:58 PM
https://www.eurekalert.org/news-releases/1123936
EXCERPTS: There are already hundreds of thousands of large language models (LLMs) in existence with a few dozen commercial systems dominating the market. Between options such as GPT-4, Claude and Gemini, many people have their favorite, especially when it comes to creative tasks such as writing.
Those preferences, however, are likely entirely in the eye of the beholder. According to new research from Duke University, the creative outputs of commercial LLMs are more similar to each other than users might hope. When challenged with three standard tasks assessing creativity, answers from commercial LLMs are much more alike than their human counterparts.
The results appeared online March 24 in the journal Proceedings of the National Academy of Sciences Nexus.
“People might wonder if different LLMs will take them in different directions with the same prompts for creative projects,” said Emily Wenger, the Cue Family Assistant Professor of Electrical and Computer Engineering at Duke. “This paper basically says no. LLMs are less creative as a population than humans.”
According to a 2024 survey by Adobe, over half of Americans have already used LLMs as creative partners for brainstorming, writing, creating images or writing code. Because an overwhelming majority of users trust them for help with being more creative, researchers have been trying to find out if that trust is misplaced.
[...] The results, which aimed to measure the variability and originality in responses between LLMs and people, were clear. While individual LLMs might outperform individual people in levels of creativity, as a whole, the algorithms’ responses were much more similar to each other than the people’s. Importantly, altering the LLM system prompt to encourage higher creativity only slightly increased their variability—and human responses still won out.
“This work has broad implications as people continue adopting and integrating LLMs into their daily life,” Wenger said. “Over reliance on these tools will smooth the world’s work toward the same underlying set of words or grammar, tending to make writing all look the same.”
“If you’re trying to come up with an original concept or product to stand out from the crowd,” Wenger continued, “this work highly suggests you should bring together a diverse group of people to brainstorm rather than relying on AI.” (MORE - missing details, no ads)
EXCERPTS: There are already hundreds of thousands of large language models (LLMs) in existence with a few dozen commercial systems dominating the market. Between options such as GPT-4, Claude and Gemini, many people have their favorite, especially when it comes to creative tasks such as writing.
Those preferences, however, are likely entirely in the eye of the beholder. According to new research from Duke University, the creative outputs of commercial LLMs are more similar to each other than users might hope. When challenged with three standard tasks assessing creativity, answers from commercial LLMs are much more alike than their human counterparts.
The results appeared online March 24 in the journal Proceedings of the National Academy of Sciences Nexus.
“People might wonder if different LLMs will take them in different directions with the same prompts for creative projects,” said Emily Wenger, the Cue Family Assistant Professor of Electrical and Computer Engineering at Duke. “This paper basically says no. LLMs are less creative as a population than humans.”
According to a 2024 survey by Adobe, over half of Americans have already used LLMs as creative partners for brainstorming, writing, creating images or writing code. Because an overwhelming majority of users trust them for help with being more creative, researchers have been trying to find out if that trust is misplaced.
[...] The results, which aimed to measure the variability and originality in responses between LLMs and people, were clear. While individual LLMs might outperform individual people in levels of creativity, as a whole, the algorithms’ responses were much more similar to each other than the people’s. Importantly, altering the LLM system prompt to encourage higher creativity only slightly increased their variability—and human responses still won out.
“This work has broad implications as people continue adopting and integrating LLMs into their daily life,” Wenger said. “Over reliance on these tools will smooth the world’s work toward the same underlying set of words or grammar, tending to make writing all look the same.”
“If you’re trying to come up with an original concept or product to stand out from the crowd,” Wenger continued, “this work highly suggests you should bring together a diverse group of people to brainstorm rather than relying on AI.” (MORE - missing details, no ads)
