# Leveraging Large Language Models for Enhanced Product Descriptions in eCommerce

arXiv: https://arxiv.org/abs/2310.18357 · HTML: https://mlsanity.com/paper/2310.18357/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Builds on (most cited first, 9 of 9)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [Controllable Paraphrase Generation with a Syntactic Exemplar](https://mlsanity.com/paper/1906.00565/) (2019)
- [RLIRank: Learning to Rank with Reinforcement Learning for Dynamic Search](https://mlsanity.com/paper/2105.10124/) (2021)
- [Diversifying Multi-aspect Search Results Using Simpson's Diversity Index](https://mlsanity.com/paper/2105.10075/) (2021)
- [De-Biased Modelling of Search Click Behavior with Reinforcement Learning](https://mlsanity.com/paper/2105.10072/) (2021)

## Built on by (most cited first, 2 of 2)

- [Enhanced E-Commerce Attribute Extraction: Innovating with Decorative Relation Correction and LLAMA 2.0-Based Annotation](https://mlsanity.com/paper/2312.06684/) (2023)
- [PRAISE: Enhancing Product Descriptions with LLM-Driven Structured Insights](https://mlsanity.com/paper/2506.17314/) (2025)

## Abstract (arXiv)

In the dynamic field of eCommerce, the quality and comprehensiveness of product descriptions are pivotal for enhancing search visibility and customer engagement. Effective product descriptions can address the 'cold start' problem, align with market trends, and ultimately lead to increased click-through rates. Traditional methods for crafting these descriptions often involve significant human effort and may lack both consistency and scalability. This paper introduces a novel methodology for automating product description generation using the LLAMA 2.0 7B language model. We train the model on a dataset of authentic product descriptions from Walmart, one of the largest eCommerce platforms. The model is then fine-tuned for domain-specific language features and eCommerce nuances to enhance its utility in sales and user engagement. We employ multiple evaluation metrics, including NDCG, customer click-through rates, and human assessments, to validate the effectiveness of our approach. Our findings reveal that the system is not only scalable but also significantly reduces the human workload involved in creating product descriptions. This study underscores the considerable potential of large language models like LLAMA 2.0 7B in automating and optimizing various facets of eCommerce platforms, offering significant business impact, including improved search functionality and increased sales.