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        <title>Let Me Teach You: Pedagogical Foundations of Feedback for Language Models</title>
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        <namePart type="given">Beatriz</namePart>
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    <name type="personal">
        <namePart type="given">Niket</namePart>
        <namePart type="family">Tandon</namePart>
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    <name type="personal">
        <namePart type="given">Tanja</namePart>
        <namePart type="family">Käser</namePart>
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    <name type="personal">
        <namePart type="given">Antoine</namePart>
        <namePart type="family">Bosselut</namePart>
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            <title>Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing</title>
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        <name type="personal">
            <namePart type="given">Yaser</namePart>
            <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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        <name type="personal">
            <namePart type="given">Yun-Nung</namePart>
            <namePart type="family">Chen</namePart>
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    <abstract>Natural Language Feedback (NLF) is an increasingly popular mechanism for aligning Large Language Models (LLMs) to human preferences. Despite the diversity of the information it can convey, NLF methods are often hand-designed and arbitrary, with little systematic grounding. At the same time, research in learning sciences has long established several effective feedback models. In this opinion piece, we compile ideas from pedagogy to introduce FELT, a feedback framework for LLMs that outlines various characteristics of the feedback space, and a feedback content taxonomy based on these variables, providing a general mapping of the feedback space. In addition to streamlining NLF designs, FELT also brings out new, unexplored directions for research in NLF. We make our taxonomy available to the community, providing guides and examples for mapping our categorizations to future research.</abstract>
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    <identifier type="doi">10.18653/v1/2024.emnlp-main.674</identifier>
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        <date>2024-11</date>
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%0 Conference Proceedings
%T Let Me Teach You: Pedagogical Foundations of Feedback for Language Models
%A Borges, Beatriz
%A Tandon, Niket
%A Käser, Tanja
%A Bosselut, Antoine
%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 borges-etal-2024-teach
%X Natural Language Feedback (NLF) is an increasingly popular mechanism for aligning Large Language Models (LLMs) to human preferences. Despite the diversity of the information it can convey, NLF methods are often hand-designed and arbitrary, with little systematic grounding. At the same time, research in learning sciences has long established several effective feedback models. In this opinion piece, we compile ideas from pedagogy to introduce FELT, a feedback framework for LLMs that outlines various characteristics of the feedback space, and a feedback content taxonomy based on these variables, providing a general mapping of the feedback space. In addition to streamlining NLF designs, FELT also brings out new, unexplored directions for research in NLF. We make our taxonomy available to the community, providing guides and examples for mapping our categorizations to future research.
%R 10.18653/v1/2024.emnlp-main.674
%U https://aclanthology.org/2024.emnlp-main.674
%U https://doi.org/10.18653/v1/2024.emnlp-main.674
%P 12082-12104

Markdown (Informal)

[Let Me Teach You: Pedagogical Foundations of Feedback for Language Models](https://aclanthology.org/2024.emnlp-main.674) (Borges et al., EMNLP 2024)

• Let Me Teach You: Pedagogical Foundations of Feedback for Language Models (Borges et al., EMNLP 2024)

ACL

• Beatriz Borges, Niket Tandon, Tanja Käser, and Antoine Bosselut. 2024. Let Me Teach You: Pedagogical Foundations of Feedback for Language Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 12082–12104, Miami, Florida, USA. Association for Computational Linguistics.