<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lee-etal-2024-crafting">
    <titleInfo>
        <title>Crafting In-context Examples according to LMs’ Parametric Knowledge</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yoonsang</namePart>
        <namePart type="family">Lee</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Pranav</namePart>
        <namePart type="family">Atreya</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xi</namePart>
        <namePart type="family">Ye</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Eunsol</namePart>
        <namePart type="family">Choi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: NAACL 2024</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Kevin</namePart>
            <namePart type="family">Duh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Helena</namePart>
            <namePart type="family">Gomez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Steven</namePart>
            <namePart type="family">Bethard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Mexico City, Mexico</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In-context learning can improve the performances of knowledge-rich tasks such as question answering. In such scenarios, in-context examples trigger a language model (LM) to surface information stored in its parametric knowledge. We study how to better construct in-context example sets, based on whether the model is aware of the in-context examples. We identify ‘known’ examples, where models can correctly answer from their parametric knowledge, and ‘unknown’ ones. Our experiments show that prompting with ‘unknown’ examples decreases the performance, potentially as it encourages hallucination rather than searching for its parametric knowledge. Constructing an in-context example set that presents both known and unknown information performs the best across diverse settings. We perform analysis on three multi-answer question answering datasets, which allows us to further study answer set ordering strategies based on the LM’s knowledge of each answer. Together, our study sheds light on how to best construct in-context example sets for knowledge-rich tasks.</abstract>
    <identifier type="citekey">lee-etal-2024-crafting</identifier>
    <identifier type="doi">10.18653/v1/2024.findings-naacl.133</identifier>
    <location>
        <url>https://aclanthology.org/2024.findings-naacl.133</url>
    </location>
    <part>
        <date>2024-06</date>
        <extent unit="page">
            <start>2069</start>
            <end>2085</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Crafting In-context Examples according to LMs’ Parametric Knowledge
%A Lee, Yoonsang
%A Atreya, Pranav
%A Ye, Xi
%A Choi, Eunsol
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F lee-etal-2024-crafting
%X In-context learning can improve the performances of knowledge-rich tasks such as question answering. In such scenarios, in-context examples trigger a language model (LM) to surface information stored in its parametric knowledge. We study how to better construct in-context example sets, based on whether the model is aware of the in-context examples. We identify ‘known’ examples, where models can correctly answer from their parametric knowledge, and ‘unknown’ ones. Our experiments show that prompting with ‘unknown’ examples decreases the performance, potentially as it encourages hallucination rather than searching for its parametric knowledge. Constructing an in-context example set that presents both known and unknown information performs the best across diverse settings. We perform analysis on three multi-answer question answering datasets, which allows us to further study answer set ordering strategies based on the LM’s knowledge of each answer. Together, our study sheds light on how to best construct in-context example sets for knowledge-rich tasks.
%R 10.18653/v1/2024.findings-naacl.133
%U https://aclanthology.org/2024.findings-naacl.133
%U https://doi.org/10.18653/v1/2024.findings-naacl.133
%P 2069-2085

Markdown (Informal)

[Crafting In-context Examples according to LMs’ Parametric Knowledge](https://aclanthology.org/2024.findings-naacl.133) (Lee et al., Findings 2024)

• Crafting In-context Examples according to LMs’ Parametric Knowledge (Lee et al., Findings 2024)

ACL

• Yoonsang Lee, Pranav Atreya, Xi Ye, and Eunsol Choi. 2024. Crafting In-context Examples according to LMs’ Parametric Knowledge. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 2069–2085, Mexico City, Mexico. Association for Computational Linguistics.