Summary of Lola: Llm-assisted Online Learning Algorithm For Content Experiments, by Zikun Ye et al.
LOLA: LLM-Assisted Online Learning Algorithm for Content Experiments
by Zikun Ye, Hema Yoganarasimhan, Yufeng Zheng
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the ability of Large Language Models (LLMs) to identify engaging headlines, leveraging a large-scale dataset from Upworthy. Three pure-LLM approaches are evaluated: prompt-based methods, embedding-based models, and fine-tuned open-source LLMs. Although these methods achieve marginally higher accuracy than random predictions, they fail to predict the best-performing headline with high accuracy. To address this, the authors introduce LOLA (Large Language Models-Assisted Online Learning Algorithm), a novel framework combining LLMs with adaptive experimentation to optimize content delivery. Numerical experiments on Upworthy data show that LOLA outperforms standard A/B test methods and pure-LLM approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to automatically choose the most engaging headlines for users. It tests three different ways of using Large Language Models (LLMs) to find the best headline, but none of them are very good at it. To solve this problem, the authors create a new algorithm called LOLA that combines LLMs with an adaptive experimentation method to optimize content delivery. This approach is more effective than current methods and can be used in various settings where firms want to maximize user engagement. |
Keywords
» Artificial intelligence » Embedding » Online learning » Prompt
