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Users find that generative AI outputs, especially in the ideation phase of creative work, can be repetitive and lack human-like diversity. This 'mode collapse' is a significant bottleneck, and even larger models don't necessarily improve diversity. Users desire tools that can produce more novel and varied ideas.
I have been consistently working on transforming marketing processes using Generative AI. While the efficiency gains are undeniable, I’ve often felt a persistent bottleneck in the "ideation" phase of creative work. The output can sometimes feel... repetitive. That is why I found the recent paper "NoveltyBench: Evaluating Language Models for Humanlike Diversity" so intriguing. It scientifically validates why we feel this way and offers a path forward. Here are my key takeaways: 💡1. The "Mode Collapse" is real: Even SOTA models significantly lag behind human diversity when generating ideas. 💡2. Bigger isn't always more creative: Surprisingly, the study found that larger, "smarter" models often yield less diversity in their outputs. The training for "correctness" may be narrowing the window for "creativity." 💡3. The Solution: Simply asking AI to "be creative" doesn't work well. The paper suggests "In-context regeneration"—explicitly including past answers and asking for something different—is the most effective way to unlock diversity. For those of us using AI for creative brainstorming, we need to shift from expecting instant magic to managing an iterative dialogue. #GenerativeAI #CreativeDirector #MarketingTransformation #LLM #AIResearch https://lnkd.in/g2N7qFDE