Machine Generated Product Advertisements: Benchmarking LLMs Against Human Performance
Not on the map. Why?
Abstract (arXiv)
This study compares the performance of AI-generated and human-written product descriptions using a multifaceted evaluation model. We analyze descriptions for 100 products generated by four AI models (Gemma 2B, LLAMA, GPT2, and ChatGPT 4) with and without sample descriptions, against human-written descriptions. Our evaluation metrics include sentiment, readability, persuasiveness, Search Engine Optimization(SEO), clarity, emotional appeal, and call-to-action effectiveness. The results indicate that ChatGPT 4 performs the best. In contrast, other models demonstrate significant shortcomings, producing incoherent and illogical output that lacks logical structure and contextual relevance. These models struggle to maintain focus on the product being described, resulting in disjointed sentences that do not convey meaningful information. This research provides insights into the current capabilities and limitations of AI in the creation of content for e-Commerce.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 4 of its 17 references that are in the map. First posted on arXiv 27 Dec 2024.
Builds on
4 of 17 references.
References · 4
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
- 2024Beyond Generative Artificial Intelligence: Roadmap for Natural Language Generationcited by 2
- 2024Exploring AI Text Generation, Retrieval-Augmented Generation, and Detection Technologies: a Comprehensive Overviewcited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.
- 2026Belief Cascades Drive Persuasion in LLM Agent Networkscited by 0
- 2026Belief Cascades Drive Persuasion in LLM Agent Networks25 Aug 2026