Multi-trait User Simulation with Adaptive Decoding for Conversational Task Assistants

TL;DR

This paper presents mTAD, a scalable method for generating diverse user profiles in conversational systems by sampling from trait-specific language models, improving robustness and diversity in user simulation.

Contribution

The paper introduces Multi-Trait Adaptive Decoding (mTAD), a novel scalable approach for user simulation that generates diverse profiles without additional fine-tuning.

Findings

mTAD effectively models single-traits with specialized LMs.

The approach captures less common dialogue patterns.

mTAD enhances diversity and robustness in user simulation.

Abstract

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 trait-specific 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 profile-aware dialogues that enhance conversational diversity. Experimental results validate the effectiveness of our approach in modeling single-traits 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.