Fine-tuning Open-source Large Language Models for Automated Response to Customer Feedback - Albuquerque, M.; Barbosa, L.; Moreira, J.; Silva, A. da; Melo, T. (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2024: Anais do XII Symposium on Knowledge Discovery, Mining and Learning, 2024)
Online reviews play a key role in influence customer decisions during their purchase journey. Consequently, negative feedback from customers can have an adverse impact on the sales of products or services, potentially leading to diminished revenue and market share. However, this effect can be mitigated by crafting thoughtful responses to these comments. This paper proposes using open-source pre-trained large language models, specifically smaller versions, to respond to negative reviews effectively. These models, pre-trained on large datasets, require minimal additional data for fine-tuning. To validate the effectiveness of this approach, we apply our solution to the domain of restaurant reviews. Our research shows that these fine-tuned models perform comparably to larger models, such as ChatGPT-3.5, in generating respectful, specific, and corrective responses that encourage customers to revisit the restaurant.