Comparative Analysis of Low-Rank Adaptation in Large Language Models versus Dense Embedding Regression for Headline Click-Through Rate Prediction
Optimizing digital content headlines for click-through rate (CTR) is an important problem in online media and recommendation systems. While large language models (LLMs) have demonstrated strong generative capabilities, their effectiveness for discriminative ranking tasks, such as selecting the highest-performing headline from a set of candidates, remains less well understood. In this work, we compare a LoRA-fine-tuned causal language model, LOLAQwen (0.6B), with a dense embedding regression model for headline selection. We formulate headline selection as a winner-take-all classification problem and evaluate both approaches using a dataset of 3,263 A/B-tested headline groups. Performance is measured using Top-1 accuracy, defined as the proportion of groups for which the model correctly identifies the highest-performing headline. The embedding regression model achieves a Top-1 accuracy of 42.79%, compared with 35.70% for the LoRA-fine-tuned language model. These results indicate that, for this headline selection task, a lightweight discriminative approach can outperform a small generative language model fine-tuned using parameter-efficient adaptation. The findings highlight the potential of embedding-based regression models as efficient alternatives to generative models for high-throughput content ranking applications.