一句话，性能暴涨49%，马里兰MIT等力作：Prompt才是大模型终极武器

Core Insights - The performance improvement of AI models is attributed equally to model upgrades and the optimization of user prompts, with 51% of the enhancement coming from the model and 49% from prompt optimization [2][28]. Group 1: Research Findings - A collaborative study by institutions such as the University of Maryland, MIT, and Stanford demonstrated that user prompts significantly influence AI performance, specifically in image generation tasks using DALL-E models [2][4]. - The concept of "prompt adaptation" was introduced, highlighting the importance of user input in maximizing the capabilities of AI models [3][12]. - The study involved 1,893 participants who generated images using DALL-E 2 and DALL-E 3, revealing that DALL-E 3 outperformed DALL-E 2 due to both model improvements and user prompt adjustments [4][21]. Group 2: Experimental Design - Participants were tasked with generating images based on specific target images, with their performance measured by the cosine similarity between generated and target images [14][15]. - The experiment aimed to separate the effects of model upgrades and prompt optimization on overall performance, using a replay analysis method to assess contributions from both factors [16][26]. - Results indicated that users of DALL-E 3 produced images with a cosine similarity average higher by 0.0164 compared to DALL-E 2 users, demonstrating the model's superior capabilities [22][25]. Group 3: User Behavior and Prompting Strategies - Users of DALL-E 3 tended to create longer and more descriptive prompts, indicating a shift in strategy as they adapted to the model's enhanced capabilities [25][30]. - The study found that the effectiveness of prompt optimization is contingent upon the model's ability to handle complex instructions, suggesting that user input must evolve alongside technological advancements [30][32]. - The research highlighted that lower-skilled users benefited more from model upgrades, while high-skilled users experienced diminishing returns, emphasizing the need for tailored prompting strategies [31][32].