Modern media firms require automated and efficient methods to identify content that is most engaging and appealing to users. Leveraging a large-scale data set from Upworthy (a news publisher), which includes 17,681 headline A/B tests, we first investigate the ability of three pure–large language model (LLM) approaches to identify the catchiest headline: prompt-based methods, embedding-based methods, and fine-tuned open-source LLMs. Prompt-based approaches perform poorly, while both OpenAI embedding–based models and the fine-tuned Llama-3-8B achieve marginally higher accuracy than random predictions. In sum, none of the pure LLM–based methods can predict the best-performing headline with high accuracy. We then introduce the LLM-assisted online learning algorithm (LOLA), a novel framework that integrates LLMs with adaptive experimentation to optimize content delivery. LOLA combines the best pure-LLM approach with the upper confidence bound algorithm to allocate traffic and maximize clicks adaptively. Our numerical experiments on Upworthy data show that LOLA outperforms the standard A/B test method (the current status quo at Upworthy), pure bandit algorithms, and pure-LLM approaches, particularly in scenarios with limited experimental traffic. Our approach is scalable and applicable to content experiments across various settings where firms seek to optimize user engagement, including digital advertising and social media recommendations.

Download the Presentation

Watch the Recording 

Read the Summary