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        <title>System-Level Natural Language Feedback</title>
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        <namePart type="given">Weizhe</namePart>
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            <title>Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
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            <namePart type="given">Yvette</namePart>
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    <abstract>Natural language (NL) feedback offers rich insights into user experience. While existing studies focus on an instance-level approach, where feedback is used to refine specific examples, we introduce a framework for system-level use of NL feedback. We show how to use feedback to formalize system-level design decisions in a human-in-the-loop-process – in order to produce better models. In particular this is done through: (i) metric design for tasks; and (ii) language model prompt design for refining model responses. We conduct two case studies of this approach for improving search query and dialog response generation, demonstrating the effectiveness of system-level feedback. We show the combination of system-level and instance-level feedback brings further gains, and that human written instance-level feedback results in more grounded refinements than GPT-3.5 written ones, underlying the importance of human feedback for building systems.</abstract>
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        <url>https://aclanthology.org/2024.eacl-long.169</url>
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%0 Conference Proceedings
%T System-Level Natural Language Feedback
%A Yuan, Weizhe
%A Cho, Kyunghyun
%A Weston, Jason
%Y Graham, Yvette
%Y Purver, Matthew
%S Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F yuan-etal-2024-system
%X Natural language (NL) feedback offers rich insights into user experience. While existing studies focus on an instance-level approach, where feedback is used to refine specific examples, we introduce a framework for system-level use of NL feedback. We show how to use feedback to formalize system-level design decisions in a human-in-the-loop-process – in order to produce better models. In particular this is done through: (i) metric design for tasks; and (ii) language model prompt design for refining model responses. We conduct two case studies of this approach for improving search query and dialog response generation, demonstrating the effectiveness of system-level feedback. We show the combination of system-level and instance-level feedback brings further gains, and that human written instance-level feedback results in more grounded refinements than GPT-3.5 written ones, underlying the importance of human feedback for building systems.
%U https://aclanthology.org/2024.eacl-long.169
%P 2773-2789

Markdown (Informal)

[System-Level Natural Language Feedback](https://aclanthology.org/2024.eacl-long.169) (Yuan et al., EACL 2024)

• System-Level Natural Language Feedback (Yuan et al., EACL 2024)

ACL

• Weizhe Yuan, Kyunghyun Cho, and Jason Weston. 2024. System-Level Natural Language Feedback. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2773–2789, St. Julian’s, Malta. Association for Computational Linguistics.