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        <title>Zero-Shot Prompting for Implicit Intent Prediction and Recommendation with Commonsense Reasoning</title>
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    <abstract>The current generation of intelligent assistants require explicit user requests to perform tasks or services, often leading to lengthy and complex conversations. In contrast, human assistants can infer multiple implicit intents from utterances via their commonsense knowledge, thereby simplifying interactions. To bridge this gap, this paper proposes a framework for multi-domain dialogue systems. This framework automatically infers implicit intents from user utterances, and prompts a large pre-trained language model to suggest suitable task-oriented bots. By leveraging commonsense knowledge, our framework recommends associated bots in a zero-shot manner, enhancing interaction efficiency and effectiveness. This approach substantially reduces interaction complexity, seamlessly integrates various domains and tasks, and represents a significant step towards creating more human-like intelligent assistants that can reason about implicit intents, offering a superior user experience.</abstract>
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%0 Conference Proceedings
%T Zero-Shot Prompting for Implicit Intent Prediction and Recommendation with Commonsense Reasoning
%A Kuo, Hui-Chi
%A Chen, Yun-Nung
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F kuo-chen-2023-zero
%X The current generation of intelligent assistants require explicit user requests to perform tasks or services, often leading to lengthy and complex conversations. In contrast, human assistants can infer multiple implicit intents from utterances via their commonsense knowledge, thereby simplifying interactions. To bridge this gap, this paper proposes a framework for multi-domain dialogue systems. This framework automatically infers implicit intents from user utterances, and prompts a large pre-trained language model to suggest suitable task-oriented bots. By leveraging commonsense knowledge, our framework recommends associated bots in a zero-shot manner, enhancing interaction efficiency and effectiveness. This approach substantially reduces interaction complexity, seamlessly integrates various domains and tasks, and represents a significant step towards creating more human-like intelligent assistants that can reason about implicit intents, offering a superior user experience.
%R 10.18653/v1/2023.findings-acl.17
%U https://aclanthology.org/2023.findings-acl.17
%U https://doi.org/10.18653/v1/2023.findings-acl.17
%P 249-258

Markdown (Informal)

[Zero-Shot Prompting for Implicit Intent Prediction and Recommendation with Commonsense Reasoning](https://aclanthology.org/2023.findings-acl.17) (Kuo & Chen, Findings 2023)

• Zero-Shot Prompting for Implicit Intent Prediction and Recommendation with Commonsense Reasoning (Kuo & Chen, Findings 2023)

ACL

• Hui-Chi Kuo and Yun-Nung Chen. 2023. Zero-Shot Prompting for Implicit Intent Prediction and Recommendation with Commonsense Reasoning. In Findings of the Association for Computational Linguistics: ACL 2023, pages 249–258, Toronto, Canada. Association for Computational Linguistics.