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

• URL: http://arxiv.org/abs/2410.12891v2
• Date: Mon, 28 Oct 2024 09:22:49 GMT
• Title: Multi-trait User Simulation with Adaptive Decoding for Conversational Task Assistants
• Authors: Rafael Ferreira, David Semedo, João Magalhães,
• Abstract summary: This paper introduces Multi-Trait Adaptive Decoding (mTAD), a method that generates diverse user profiles at decoding-time. By analyzing real-world dialogues from the Conversational Task Assistant domain, we identify key conversational traits. We develop a framework to generate profile-aware dialogues that enhance conversational diversity.
• Score: 11.092533523906834
• License:
• 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.

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