Multi-trait User Simulation with Adaptive Decoding for Conversational Task Assistants
Rafael Ferreira, David Semedo, João Pedro de Magalhães · 2024
Conversational systems must be robust to user interactions that naturally exhibit diverse conversational traits.Capturing and simulating these diverse traits coherently and efficiently presents a complex challenge.This paper introduces Multi-Trait Adaptive Decoding (mTAD), a method that generates diverse user profiles at decoding-time by sampling from various traitspecific Language Models (LMs).mTAD provides an adaptive and scalable approach to user simulation, enabling the creation of multiple user profiles without the need for additional fine-tuning.By analyzing real-world dialogues from the Conversational Task Assistant (CTA) domain, we identify key conversational traits and developed a framework to generate profileaware dialogues that enhance conversational diversity.Experimental results validate the effectiveness of our approach in modeling singletraits using specialized LMs, which can capture less common patterns, even in out-of-domain tasks.Furthermore, the results demonstrate that mTAD is a robust and flexible framework for combining diverse user simulators.