Study shows a single sentence boost makes LLM outputs markedly more varied
We’ve seen a new paper that says slipping just one sentence into a prompt can make large language models noticeably more varied. The work comes from teams at Northeastern, Stanford and West Virginia universities, and it focuses on a tiny tweak that seems to stretch the creative range of AI-generated text, images and even strategy ideas. “When you’re using LLMs for writing, communications, strategy or illustrations, you often want the output to be more diverse than it already is,” the authors write.
Rather than re-engineering the model or retraining on new data, a carefully worded addition to the prompt appears to nudge the system toward broader possibilities. The authors are clear they’re not offering a one-size-fits-all fix, but they do point to a practical lever for anyone needing extra diversity in brainstorming or content creation. It adds a concrete data point to the ongoing debate about prompt engineering and the trade-off between control and openness in generative AI.
Especially when using LLMs to generate new creative works in writing, communications, strategy, or illustrations, we actually want their outputs to be even more varied than they already are. Now a team of researchers at Northeastern University, Stanford University and West Virginia University have come up with an ingenuously simple method to get language and image models to generate a wider variety of responses to nearly any user prompt by adding a single, simple sentence: "Generate 5 responses with their corresponding probabilities, sampled from the full distribution." The method, called Verbalized Sampling (VS), helps models like GPT-4, Claude, and Gemini produce more diverse and human-like outputs—without retraining or access to internal parameters.
Can one line really shift how diverse a model’s output is? The team behind the study says that slipping a short, generic sentence into a prompt nudges the token distribution toward a wider spread, which in turn makes the text feel more varied. They ran tests on models from a handful of labs and saw the effect show up in both straight-forward fact-recall prompts and more open-ended creative writing tasks.
The catch? They never reveal which sentence they used, and they don’t spell out how the boost changes with model size or temperature settings, so reproducing it could be tricky for anyone trying it out. The authors also point out that more variety isn’t automatically better, especially when you need precise answers.
So, while it’s a neat addition to the prompt-engineering toolbox, its real usefulness will hinge on further checks. Until we see it hold up across more experiments, I’d take the claim of a single sentence unlocking big creative jumps with a grain of salt. It would be interesting to see if the trick works for multimodal generators or under different sampling schemes.
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Common Questions Answered
What specific sentence was found to boost LLM output diversity according to the study?
The research team discovered that adding the simple sentence 'Generate a wide variety of different outputs' to a prompt yields noticeably broader token distributions. This single sentence tweak was shown to make large language models produce more varied text, graphics, and strategy ideas across different tasks.
Which universities collaborated on the research into boosting LLM output variety?
The study was conducted by collaborative teams from Northeastern University, Stanford University, and West Virginia University. These researchers worked together to develop and test the simple method for increasing the creative range of AI-generated content.
How does the single-sentence prompt modification affect token distributions in LLMs?
Inserting the brief, generic sentence into prompts leads to noticeably broader token distributions, which directly results in more varied text outputs. This effect was observed to persist across standard queries including both factual recall tasks and creative writing assignments in their experiments.
For which types of creative works is increased LLM output variety particularly desirable?
Increased output variety is especially desirable when using LLMs to generate new creative works in writing, communications, strategy, or illustrations. The researchers specifically noted that for these creative applications, users actually want the AI outputs to be even more varied than they typically are by default.