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        <title>Enhancing AI Assisted Writing with One-Shot Implicit Negative Feedback</title>
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    <abstract>AI-mediated communication enables users to communicate more quickly and efficiently. Various systems have been proposed such as smart reply and AI-assisted writing. Yet, the heterogeneity of the forms of inputs and architectures often renders it challenging to combine insights from user behaviour in one system to improve performance in another. In this work, we consider the case where the user does not select any of the suggested replies from a smart reply system, and how this can be used as one-shot implicit negative feedback to enhance the accuracy of an AI writing model. We introduce Nifty, an approach that uses classifier guidance to controllably integrate implicit user feedback into the text generation process. Empirically, we find up to 34% improvement in Rouge-L, 89% improvement in generating the correct intent, and an 86% win-rate according to human evaluators compared to a vanilla AI writing system on the MultiWOZ and Schema-Guided Dialog datasets. The code is available at https://github.com/BenjaminTowle/NIFTY.</abstract>
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%0 Conference Proceedings
%T Enhancing AI Assisted Writing with One-Shot Implicit Negative Feedback
%A Towle, Benjamin
%A Zhou, Ke
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F towle-zhou-2024-enhancing
%X AI-mediated communication enables users to communicate more quickly and efficiently. Various systems have been proposed such as smart reply and AI-assisted writing. Yet, the heterogeneity of the forms of inputs and architectures often renders it challenging to combine insights from user behaviour in one system to improve performance in another. In this work, we consider the case where the user does not select any of the suggested replies from a smart reply system, and how this can be used as one-shot implicit negative feedback to enhance the accuracy of an AI writing model. We introduce Nifty, an approach that uses classifier guidance to controllably integrate implicit user feedback into the text generation process. Empirically, we find up to 34% improvement in Rouge-L, 89% improvement in generating the correct intent, and an 86% win-rate according to human evaluators compared to a vanilla AI writing system on the MultiWOZ and Schema-Guided Dialog datasets. The code is available at https://github.com/BenjaminTowle/NIFTY.
%U https://aclanthology.org/2024.emnlp-main.705
%P 12672-12680

Markdown (Informal)

[Enhancing AI Assisted Writing with One-Shot Implicit Negative Feedback](https://aclanthology.org/2024.emnlp-main.705) (Towle & Zhou, EMNLP 2024)

• Enhancing AI Assisted Writing with One-Shot Implicit Negative Feedback (Towle & Zhou, EMNLP 2024)

ACL

• Benjamin Towle and Ke Zhou. 2024. Enhancing AI Assisted Writing with One-Shot Implicit Negative Feedback. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 12672–12680, Miami, Florida, USA. Association for Computational Linguistics.