<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="owers-etal-2026-easy">
    <titleInfo>
        <title>As Easy as Rocket Science: Assessing the Ability of Large Language Models to Interpret Negation in Figurative Language</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jasmine</namePart>
        <namePart type="family">Owers</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Edwin</namePart>
        <namePart type="family">Simpson</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Martha</namePart>
        <namePart type="family">Lewis</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <genre authority="bibutilsgt">journal article</genre>
    <relatedItem type="host">
        <titleInfo>
            <title>Transactions of the Association for Computational Linguistics</title>
        </titleInfo>
        <originInfo>
            <issuance>continuing</issuance>
            <publisher>MIT Press</publisher>
            <place>
                <placeTerm type="text">Cambridge, MA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">periodical</genre>
        <genre authority="bibutilsgt">academic journal</genre>
    </relatedItem>
    <abstract>Figurative language and negation are two areas that challenge current language models, however, both are widely used throughout written and spoken language. Large language models (LLMs) are also widely used in everyday contexts where they cannot necessarily be tuned for a specific dataset. It is therefore essential to understand the ability of LLMs to correctly interpret text that includes both negation and figurative language. To investigate this, we develop a set of new annotations to an existing dataset of figurative language, and test a range of language models on the dataset. We find that the combination of negation and figurativeness can present a particular challenge, and that performance overall and across different negation types is particularly dependent on the prompt style used.</abstract>
    <identifier type="citekey">owers-etal-2026-easy</identifier>
    <identifier type="doi">10.1162/tacl.a.781</identifier>
    <location>
        <url>https://aclanthology.org/2026.tacl-1.83/</url>
    </location>
    <part>
        <date>2026</date>
        <detail type="volume"><number>14</number></detail>
        <extent unit="page">
            <start>1851</start>
            <end>1866</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Journal Article
%T As Easy as Rocket Science: Assessing the Ability of Large Language Models to Interpret Negation in Figurative Language
%A Owers, Jasmine
%A Simpson, Edwin
%A Lewis, Martha
%J Transactions of the Association for Computational Linguistics
%D 2026
%V 14
%I MIT Press
%C Cambridge, MA
%F owers-etal-2026-easy
%X Figurative language and negation are two areas that challenge current language models, however, both are widely used throughout written and spoken language. Large language models (LLMs) are also widely used in everyday contexts where they cannot necessarily be tuned for a specific dataset. It is therefore essential to understand the ability of LLMs to correctly interpret text that includes both negation and figurative language. To investigate this, we develop a set of new annotations to an existing dataset of figurative language, and test a range of language models on the dataset. We find that the combination of negation and figurativeness can present a particular challenge, and that performance overall and across different negation types is particularly dependent on the prompt style used.
%R 10.1162/tacl.a.781
%U https://aclanthology.org/2026.tacl-1.83/
%U https://doi.org/10.1162/tacl.a.781
%P 1851-1866

Markdown (Informal)

[As Easy as Rocket Science: Assessing the Ability of Large Language Models to Interpret Negation in Figurative Language](https://aclanthology.org/2026.tacl-1.83/) (Owers et al., TACL 2026)

• As Easy as Rocket Science: Assessing the Ability of Large Language Models to Interpret Negation in Figurative Language (Owers et al., TACL 2026)

ACL

• Jasmine Owers, Edwin Simpson, and Martha Lewis. 2026. As Easy as Rocket Science: Assessing the Ability of Large Language Models to Interpret Negation in Figurative Language. Transactions of the Association for Computational Linguistics, 14:1851–1866.