Intent Recognition Optimization Using Multimodal Cloud-Based Genai Models

Abstract

Intent recognition is a critical component of various Natural Language Processing (NLP) applications, including virtual assistants, chatbots, and customer service systems. Traditional methods often rely on single-modal inputs, such as text alone, leading to limitations in understanding user intent in diverse and complex real-world scenarios. In this paper, we propose an optimization approach for intent recognition by leveraging multimodal cloud-based Generative AI (GenAI) models. These models integrate multiple input modalities, including text, audio, and visual data, enabling more accurate and contextually aware recognition of user intent. By utilizing cloud computing resources, we scale the training and inference processes of GenAI models, ensuring efficient deployment for real-time applications. Our methodology focuses on the optimization of data processing, feature extraction, and fusion techniques to improve model performance. We conduct experiments comparing multimodal GenAI models with baseline single-modal approaches, showcasing significant improvements in intent recognition accuracy and robustness. The findings indicate that multimodal GenAI models, when optimized, offer superior intent recognition capabilities, especially in complex, noisy, or ambiguous contexts. This work presents a significant step towards enhancing the effectiveness of AI systems in understanding user intentions across a range of multimodal inputs.