<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="cho-etal-2024-tutor">
    <titleInfo>
        <title>Tutor-ICL: Guiding Large Language Models for Improved In-Context Learning Performance</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ikhyun</namePart>
        <namePart type="family">Cho</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Gaeul</namePart>
        <namePart type="family">Kwon</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Julia</namePart>
        <namePart type="family">Hockenmaier</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2024</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yaser</namePart>
            <namePart type="family">Al-Onaizan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mohit</namePart>
            <namePart type="family">Bansal</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yun-Nung</namePart>
            <namePart type="family">Chen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Miami, Florida, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>There has been a growing body of work focusing on the in-context learning (ICL) abilities of large language models (LLMs). However, it is an open question how effective ICL can be. This paper presents Tutor-ICL, a simple prompting method for classification tasks inspired by how effective instructors might engage their students in learning a task. Specifically, we propose presenting exemplar answers in a *comparative format* rather than the traditional single-answer format. We also show that including the test instance before the exemplars can improve performance, making it easier for LLMs to focus on relevant exemplars. Lastly, we include a summarization step before attempting the test, following a common human practice. Experiments on various classification tasks, conducted across both decoder-only LLMs (Llama 2, 3) and encoder-decoder LLMs (Flan-T5-XL, XXL), show that Tutor-ICL consistently boosts performance, achieving up to a 13.76% increase in accuracy.</abstract>
    <identifier type="citekey">cho-etal-2024-tutor</identifier>
    <location>
        <url>https://aclanthology.org/2024.findings-emnlp.554</url>
    </location>
    <part>
        <date>2024-11</date>
        <extent unit="page">
            <start>9496</start>
            <end>9506</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Tutor-ICL: Guiding Large Language Models for Improved In-Context Learning Performance
%A Cho, Ikhyun
%A Kwon, Gaeul
%A Hockenmaier, Julia
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F cho-etal-2024-tutor
%X There has been a growing body of work focusing on the in-context learning (ICL) abilities of large language models (LLMs). However, it is an open question how effective ICL can be. This paper presents Tutor-ICL, a simple prompting method for classification tasks inspired by how effective instructors might engage their students in learning a task. Specifically, we propose presenting exemplar answers in a *comparative format* rather than the traditional single-answer format. We also show that including the test instance before the exemplars can improve performance, making it easier for LLMs to focus on relevant exemplars. Lastly, we include a summarization step before attempting the test, following a common human practice. Experiments on various classification tasks, conducted across both decoder-only LLMs (Llama 2, 3) and encoder-decoder LLMs (Flan-T5-XL, XXL), show that Tutor-ICL consistently boosts performance, achieving up to a 13.76% increase in accuracy.
%U https://aclanthology.org/2024.findings-emnlp.554
%P 9496-9506

Markdown (Informal)

[Tutor-ICL: Guiding Large Language Models for Improved In-Context Learning Performance](https://aclanthology.org/2024.findings-emnlp.554) (Cho et al., Findings 2024)

• Tutor-ICL: Guiding Large Language Models for Improved In-Context Learning Performance (Cho et al., Findings 2024)

ACL

• Ikhyun Cho, Gaeul Kwon, and Julia Hockenmaier. 2024. Tutor-ICL: Guiding Large Language Models for Improved In-Context Learning Performance. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 9496–9506, Miami, Florida, USA. Association for Computational Linguistics.