Uncertainty-aware augmented generation (UAG): A novel deep learning method for enriching in-conversation user intent toward improved LLM generation

Xulei Jin, Lihua Huang, Tan Cheng, Shuaiyong Xiao, Chenghong Zhang, Yajing Wang,
Uncertainty-aware augmented generation (UAG): A novel deep learning method for enriching in-conversation user intent toward improved LLM generation,
Decision Support Systems,
Volume 199,
2025,
114558,
ISSN 0167-9236,
https://doi.org/10.1016/j.dss.2025.114558.
(https://www.sciencedirect.com/science/article/pii/S0167923625001599)
Abstract: In the era of artificial intelligence generated content, accurate user intent detection and effective response generation have become critical capabilities for LLM-based service agents. However, due to users' limited familiarity with domain-specific knowledge, their underspecified queries often introduce intent uncertainty, impeding the generation of responses that are both contextually relevant and operationally executable. To address this challenge, we propose uncertainty-aware augmented generation (UAG), a novel deep learning method that jointly detects user intents and quantifies their associated uncertainty, thereby bridging the gap between user queries and enterprise-executable actions. UAG enhances intent detection along a predefined intent tree by incorporating two hierarchical consistency losses, and improves the quality of generated responses by leveraging salient intent paths—extracted using a proposed uncertainty-aware intent (UI) score—as an augmented prompt. Experiment results based on two datasets showed that UAG outperformed state-of-the-art alternative benchmarks, and explanatory analysis rendered insight on the role of uncertainty in user intent detection and response generation.
Keywords: Intent detection; Intent uncertainty; Uncertainty-aware intent path; Uncertainty-aware augmented generation; LLM-based service agents