LLMs are stuck in a groupthink groove. This startup is trying to get them out.
MIT Technology Review AI Will Douglas Heaven
Australian startup Springboards built an LLM called Flint that generates more diverse responses to open-ended questions than mainstream models, addressing a widespread tendency for language models to converge on similar, predictable answers. A November NeurIPS paper titled "Artificial Hivemind" demonstrated that when 25 different LLMs were asked 50 times each to write a metaphor about time, most of the 1,250 responses were variations of "Time is a river" or "Time is a weaver." Springboards trained Flint to identify specific points in its output where variety is possible and inject less predictable words at those moments, allowing creative professionals in advertising and marketing to access more divergent ideas for brainstorming.
Why it matters
Let’s start with a game. Open up your chatbot of choice—Claude, ChatGPT, Gemini—and type “Give me a random number between 1 and 10.” You’re going to get 7. Almost always. Now type “Another” and you’ll get 3 or 4. Type “Another” again and you’ll get 8 or 9. That won’t work every time—but if it…
