Optimizing Conversational Commerce Involving Multilingual Consumers Through Large Language Models’ Natural Language Understanding Abilities

Joseph Benjamin Ilagan, Jose Ramon Ilagan, Pia Ysabel Zulueta, Maria Mercedes T. Rodrigo

In Artificial Intelligence in HCI (HCII 2024), LNCS, pp. 47–59. Springer, 2024.

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Abstract

Due to the emergence of natural language processing (NLP) interfaces, there has been growing intent to use conversational channels for commerce. Beyond customer service, NLP-enabled AI agents are being integrated into various steps of the order-to-cash (OTC) process. Social media and messaging platforms such as Facebook Messenger have become pivotal for businesses, especially during and after the COVID-19 pandemic, but adoption has been limited. In addition, attitudes towards fully-automated conversational agents (CA) have been mixed, and there is room for human involvement in transactional conversations. A distinguishing contribution of this research is leveraging the inherent capabilities of Large Language Models (LLMs) in handling multilingual conversations and extracting transactional details through named entity recognition (NER). The study describes a hybrid human-AI setup augmenting agents with an auto-agent leveraging LLMs’ natural language understanding (NLU) capabilities, designed using the OTC process pattern applied to conversational UX frameworks. A prototype of the setup aims to streamline operations and reduce errors by enhancing the user experience during key OTC steps through improved conversational design. Recognizing the irreplaceable essence of human interaction, the hybrid human-in-the-loop approach was chosen, mitigating the impersonal nature of full automation. A prototype handling customers and humans augmented by LLMs for NER handling of transaction, customer, and product information was built. Sample synthetic bilingual conversations between customers and sales agents were generated using ChatGPT and fed into the system for evaluation.

Keywords: Conversational user experience; conversational commerce; Large language models; Natural language understanding; Named entity recognition; Generative AI; Co-pilots

About this copy

Accepted manuscript. This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-60615-1_4. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms

Cite as

Joseph Benjamin Ilagan, Jose Ramon Ilagan, Pia Ysabel Zulueta, Maria Mercedes T. Rodrigo (2024). Optimizing Conversational Commerce Involving Multilingual Consumers Through Large Language Models’ Natural Language Understanding Abilities. In Artificial Intelligence in HCI (HCII 2024), LNCS, pp. 47–59. Springer. https://doi.org/10.1007/978-3-031-60615-1_4