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        <title>Multi-trait User Simulation with Adaptive Decoding for Conversational Task Assistants</title>
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        <namePart type="given">Rafael</namePart>
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        <namePart type="given">Joao</namePart>
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            <namePart type="family">Al-Onaizan</namePart>
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        <name type="personal">
            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
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            <namePart type="given">Yun-Nung</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Multi-trait User Simulation with Adaptive Decoding for Conversational Task Assistants
%A Ferreira, Rafael
%A Semedo, David
%A Magalhaes, Joao
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F ferreira-etal-2024-multi
%X 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.
%U https://aclanthology.org/2024.findings-emnlp.945
%P 16105-16130

Markdown (Informal)

[Multi-trait User Simulation with Adaptive Decoding for Conversational Task Assistants](https://aclanthology.org/2024.findings-emnlp.945) (Ferreira et al., Findings 2024)

• Multi-trait User Simulation with Adaptive Decoding for Conversational Task Assistants (Ferreira et al., Findings 2024)

ACL

• Rafael Ferreira, David Semedo, and Joao Magalhaes. 2024. Multi-trait User Simulation with Adaptive Decoding for Conversational Task Assistants. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 16105–16130, Miami, Florida, USA. Association for Computational Linguistics.