Holistic Intent Detection With LLM for Emotion AI

Nowadays, the pursuit of more empathetic interactions in Emotion AI presents intent detection with increasingly realistic and complex challenges. In this article, we propose a new research task called holistic intent detection (HID), where following five cases are involved: known, unknown, zero–shot, multi-label intents, and intent dependency. Previous studies focus on combinations of no more than three cases. Moreover, when a user conveys multiple intents, existing methods usually only focus on which intents the user expresses, while overlooking the dependencies between intents. To complete the HID task, using a pipeline to concatenate different models is theoretically feasible but impractical due to the low recognition accuracy and error propagation. The powerful reasoning ability shown by large language models (LLMs) makes it possible to complete this task. However, closed-source LLMs exist privacy and cost issues, while there is no effective fine-tuning method when using open-source LLMs with small parameter size for the HID task. Hence, we propose a LLM-based HID framework to recognize holistic intents and their dependency. First, we design a prompt template that considers all cases. Then, different intent selection strategies are used to control the prompt length in the fine-tuning and inference phases to solve the performance degradation caused by lengthy prompts. Experimental results on three datasets demonstrate that our fine-tuned open-source LLM with small parameters outperforms GPT-3.5-turbo by an average of 17.19% in overall accuracy

Inference · Task Analysis · Emotion and Mood Recognition · Explainable Artificial Intelligence (XAI · Mental Health via Writing