LOLA: LLM-Assisted Online Learning Algorithm for Content Experiments

In the rapidly evolving digital content landscape, media firms and news publishers require automated and efficient methods to enhance user engagement. This paper introduces the LLM-Assisted Online Learning Algorithm (LOLA), a novel framework that integrates Large Language Models (LLMs) with adaptive experimentation to optimize content delivery. Leveraging a large-scale dataset from Upworthy, which includes 17,681 headline A/B tests aimed at evaluating the performance of various headlines associated with the same article content, we first investigate three broad pure-LLM approaches: prompt-based methods, embedding-based classification models, and fine-tuned open-source LLMs. Our findings indicate that prompt-based approaches perform poorly, achieving no more than 65% accuracy in identifying the catchier headline among two options. In contrast, OpenAI-embedding-based classification models and fine-tuned Llama-3-8b models achieve comparable accuracy, around 82-84%, though still falling short of the performance of experimentation with sufficient traffic. We then introduce LOLA, which combines the best pure-LLM approach with the Upper Confidence Bound algorithm to adaptively allocate traffic and maximize clicks. Our numerical experiments on Upworthy data show that LOLA outperforms the standard A/B testing method (the current status quo at Upworthy), pure bandit algorithms, and pure-LLM approaches, particularly in scenarios with limited experimental traffic or numerous arms. Our approach is both scalable and broadly applicable to content experiments across a variety of digital settings where firms seek to optimize user engagement, including digital advertising and social media recommendations.