Leveraging Large Language Models for Enhanced Product Descriptions in e C ommerce (2023)

Abstract: 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.

## introduction the advent of ecommerce has revolutionized the way consumers engage with products, making online visibility and customer interaction crucial aspects for business success. a central element to this online interaction is the product description, which significantly influences search visibility and customer engagement @xcite . historically, the creation of effective product descriptions has been a manual, labor-intensive process with a tendency to lack both consistency and scalability @xcite . moreover, novel products often face the 'cold start' problem, where they lack sufficient engagement data to be adequately featured or recommended by ecommerce platforms @xcite . effective product descriptions have the potential to mitigate this issue by aligning with current market trends, thereby enhancing clickthrough rates @xcite . to address the existing challenges in ecommerce, this paper introduces an innovative methodology that employs the llama 2.0 7b language model to automate the generation of product descriptions @xcite . we begin by training the model on a carefully curated dataset of authentic product descriptions from walmart, a global leader in the ecommerce arena @xcite . during the initial training phase, we identify items with high recent clickthrough rates and use their product descriptions as positive training samples. conversely, items with lower engagement rates are used as negative training samples. for the fine-tuning process, we focus on five specific aspects of the product description: language appeal, factual information, product dimensions, unique attributes, and brand-related guarantees. the fine-tuned model aims to incorporate language that captures consumer interest while providing essential information for informed product selection @xcite . this nuanced approach significantly enhances the model's ability to boost both sales and customer engagement @xcite . in the second phase of our methodology, we target items that have lackluster product descriptions for enrichment. utilizing the fine-tuned model, we augment these descriptions by emphasizing the aforementioned key aspects. we validate the effectiveness of our approach using a comprehensive set of evaluation metrics, including normalized discounted cumulative gain (ndcg), customer click-through rates, and human evaluations. these metrics affirm the scalability and efficacy of our proposed methodology. this research makes several groundbreaking contributions to the field of automated product description generation, particularly in the context of realworld ecommerce platforms. these are as follows: ## related work natural language processing (nlp) has seen substantial advancements in recent years, thanks partly to the development of large language models (llms). these models have applications in various domains, from machine translation to sentiment analysis @xcite @xcite . however, our work uniquely contributes to this landscape by focusing on the specific use case of automated product description generation for e-commerce platforms. ## methodology our methodology employs a specialized, multifaceted approach for the automated generation of product descriptions, specifically targeting five key aspects: language appeal, factual information, product dimensions, unique attributes, and brandrelated guarantees. the methodology is implemented in three main phases: aspect-based segmentation, aspect-oriented fine-tuning, and description assembly & evaluation. ## ablation study in our endeavor to understand the impact of the hyperparameter λ on our model's performance, we conducted an ablation study. the parameter λ plays a pivotal role in modulating the trade-off between the model's objectives, which has significant implications for its efficacy in generating relevant product descriptions. referring to figure 2 , it is evident that the bm25 score exhibits an optimal value at λ = 0.429. intuitively, this demonstrates that a careful balance between our model's objectives, modulated by λ, is crucial for achieving the best results. beyond this point, it's possible that the model over-prioritizes one objective over the other, leading to sub-optimal performance. the noise in the graph and the shaded region representing one standard deviation provide insights into the inherent variability of real-world data and underline the robustness of our results @xcite . ## conclusion in this work, we have investigated the potential of state-of-the-art language models, with a particular focus on the llama 2.0 7b, for the purpose of enhancing product descriptions in e-commerce platforms. our methodology incorporated a dataset from walmart, and we employed a differentiated strategy for model training using both high and low engagement product descriptions. the framework we introduced prioritizes five essential aspects of product descriptions, facilitating a more structured and targeted approach to descrip-tion enhancement. through empirical evaluations, it was observed that the bm25 and ndcg scores for descriptions improved post-enhancement, indicating the potential of our model in terms of improving semantic relevance and overall user engagement. furthermore, our ablation study on the hyperparameter λ has provided an understanding of its influence on the bm25 scores, showcasing the importance of fine-tuning model parameters to achieve optimal performance. the nuanced observations from this study are significant for researchers aiming to optimize language models for similar tasks. in summation, this research contributes to the growing body of knowledge surrounding the application of large language models in practical ecommerce scenarios. while the results presented are promising, they also pave the way for further investigations, especially in the realm of nlp-driven automated content generation.