Hey there! If you’re interested in how AI can spice up creative writing, I’ve got some exciting news. Researchers from Midjourney and New York University have come up with a new way to make AI-generated text more diverse without sacrificing quality. They’ve just published a paper detailing how they use “deviation metrics” in AI training. This technique measures how different the texts are for the same prompts by calculating the pairwise cosine distance of embedded texts. It’s a fancy way of saying they found a systematic method to quantify text variation.
Early results are promising. Models trained with this method produce text that’s 23% more diverse, with only a slight 5% drop in quality, according to Reddit’s reward system. For example, when given the prompt, “Why are you shaking, my love? You’re king now,” the usual GPT-4o model mostly churned out stories about nervous new rulers. On the other hand, the smaller modified Llama-3.1-8B model came up with a variety of tales, from dark fantasies about bear princes to supernatural underwater adventures.
Human testers backed up these findings, noting the increased variety didn’t come at the cost of quality. However, it’s worth mentioning that this research has only been compared to the older GPT-4o model, not the newer GPT-4.5, which is known for its more natural language outputs.
The study zoomed in on two types of diversity: semantic, which deals with different story plots, and stylistic, which involves varying writing styles. The researchers found that combining these elements worked best. They used data from over 100,000 prompt-response pairs from Reddit’s r/WritingPrompts, discovering that significant diversity could be achieved with just four different responses per prompt.
While the results are promising, there are still a few unknowns. The team hasn’t tested how well this method works outside creative writing or in online training settings typical for large models. Plus, relying on Reddit upvotes for quality measurement has its downsides, as it misses out on factors like technical accuracy and professional writing standards.
Despite these challenges, this technique could revolutionize how language models handle creative writing tasks, helping them avoid the repetitive patterns we often see. The researchers are planning to share their code on GitHub so others can dive in and explore further.
