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        <title>A Prompting Assignment for Exploring Pretrained LLMs</title>
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    <name type="personal">
        <namePart type="given">Carolyn</namePart>
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            <title>Proceedings of the Sixth Workshop on Teaching NLP</title>
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        <name type="personal">
            <namePart type="given">Sana</namePart>
            <namePart type="family">Al-azzawi</namePart>
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        <name type="personal">
            <namePart type="given">Laura</namePart>
            <namePart type="family">Biester</namePart>
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        <name type="personal">
            <namePart type="given">György</namePart>
            <namePart type="family">Kovács</namePart>
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        <name type="personal">
            <namePart type="given">Ana</namePart>
            <namePart type="family">Marasović</namePart>
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        <name type="personal">
            <namePart type="given">Leena</namePart>
            <namePart type="family">Mathur</namePart>
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        <name type="personal">
            <namePart type="given">Margot</namePart>
            <namePart type="family">Mieskes</namePart>
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        <name type="personal">
            <namePart type="given">Leonie</namePart>
            <namePart type="family">Weissweiler</namePart>
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    <abstract>As the scale of publicly-available large language models (LLMs) has increased, so has interest in few-shot prompting methods. This paper presents an assignment that asks students to explore three aspects of large language model capabilities (commonsense reasoning, factuality, and wordplay) with a prompt engineering focus. The assignment consists of three tasks designed to share a common programming framework, so that students can reuse and adapt code from earlier tasks. Two of the tasks also involve dataset construction: students are asked to construct a simple dataset for the wordplay task, and a more challenging dataset for the factuality task. In addition, the assignment includes reflection questions that ask students to think critically about what they observe.</abstract>
    <identifier type="citekey">anderson-2024-prompting-assignment</identifier>
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        <url>https://aclanthology.org/2024.teachingnlp-1.12</url>
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        <date>2024-08</date>
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            <start>81</start>
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%0 Conference Proceedings
%T A Prompting Assignment for Exploring Pretrained LLMs
%A Anderson, Carolyn
%Y Al-azzawi, Sana
%Y Biester, Laura
%Y Kovács, György
%Y Marasović, Ana
%Y Mathur, Leena
%Y Mieskes, Margot
%Y Weissweiler, Leonie
%S Proceedings of the Sixth Workshop on Teaching NLP
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F anderson-2024-prompting-assignment
%X As the scale of publicly-available large language models (LLMs) has increased, so has interest in few-shot prompting methods. This paper presents an assignment that asks students to explore three aspects of large language model capabilities (commonsense reasoning, factuality, and wordplay) with a prompt engineering focus. The assignment consists of three tasks designed to share a common programming framework, so that students can reuse and adapt code from earlier tasks. Two of the tasks also involve dataset construction: students are asked to construct a simple dataset for the wordplay task, and a more challenging dataset for the factuality task. In addition, the assignment includes reflection questions that ask students to think critically about what they observe.
%U https://aclanthology.org/2024.teachingnlp-1.12
%P 81-84

Markdown (Informal)

[A Prompting Assignment for Exploring Pretrained LLMs](https://aclanthology.org/2024.teachingnlp-1.12) (Anderson, TeachingNLP-WS 2024)

• A Prompting Assignment for Exploring Pretrained LLMs (Anderson, TeachingNLP-WS 2024)

ACL

• Carolyn Anderson. 2024. A Prompting Assignment for Exploring Pretrained LLMs. In Proceedings of the Sixth Workshop on Teaching NLP, pages 81–84, Bangkok, Thailand. Association for Computational Linguistics.