Fine-Tuning Llama 2 forAutomatedAd Caption Generation

Generating engaging and relevant ad captions poses a significant challenge for advertisers. This research addresses this issue by improving Llama2, an advanced language model, through fine- tuning with a custom dataset created specifically for ad captioning. Techniques such as quantization and matrix decomposition were employed to enhance Llama2's ability to produce captivating and descriptive ad captions.The primary objective was to streamline and improve the efficiency of the caption creation process. Performance evaluation was conducted via A/B testing, comparing our enhanced Llama2 against conventional captioning methods. Key performance indicators included click- through rates, user engagement, and actionstaken,such as purchases.Experimental results demonstrated that the fine-tuned Llama2 effectively generates captions that resonate with audiences, encouraging actionable responses. This study advances the capabilities of language models in advertising and provides valuable insights for marketers looking to enhance the impact of their ad campaigns in the digital landscape.