<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="momen-zarriess-2026-frequency">
    <titleInfo>
        <title>The Frequency Confound in Language-Model Surprisal and Metaphor Novelty</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Omar</namePart>
        <namePart type="family">Momen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sina</namePart>
        <namePart type="family">Zarrieß</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*SEM 2026)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Saif</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Mohammad</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nedjma</namePart>
            <namePart type="family">Ousidhoum</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">San Diego, California, United States</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-413-2</identifier>
    </relatedItem>
    <abstract>Language-model (LM) surprisal is widely used as a proxy for contextual predictability and has been reported to correlate with metaphor novelty judgments. However, surprisal is tightly intertwined with lexical frequency. We explore this interaction on metaphor novelty ratings using two different word frequency measures. We analyse surprisal estimates from eight Pythia model sizes and 154 training checkpoints. Across settings, word frequency is a stronger predictor of metaphor novelty than surprisal. Across training stages, the surprisal–novelty association peaks at an early stage and then falls again, mirroring a similarly timed increase in the surprisal–frequency association. These results suggest that the often-reported optimal LM surprisal settings may incorrectly associate contextual predictability with metaphor novelty and processing difficulty, whereas lexical frequency may be the major underlying factor.</abstract>
    <identifier type="citekey">momen-zarriess-2026-frequency</identifier>
    <location>
        <url>https://aclanthology.org/2026.starsem-conference.31/</url>
    </location>
    <part>
        <date>2026-07</date>
        <extent unit="page">
            <start>454</start>
            <end>463</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T The Frequency Confound in Language-Model Surprisal and Metaphor Novelty
%A Momen, Omar
%A Zarrieß, Sina
%Y Mohammad, Saif M.
%Y Ousidhoum, Nedjma
%S Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*SEM 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-413-2
%F momen-zarriess-2026-frequency
%X Language-model (LM) surprisal is widely used as a proxy for contextual predictability and has been reported to correlate with metaphor novelty judgments. However, surprisal is tightly intertwined with lexical frequency. We explore this interaction on metaphor novelty ratings using two different word frequency measures. We analyse surprisal estimates from eight Pythia model sizes and 154 training checkpoints. Across settings, word frequency is a stronger predictor of metaphor novelty than surprisal. Across training stages, the surprisal–novelty association peaks at an early stage and then falls again, mirroring a similarly timed increase in the surprisal–frequency association. These results suggest that the often-reported optimal LM surprisal settings may incorrectly associate contextual predictability with metaphor novelty and processing difficulty, whereas lexical frequency may be the major underlying factor.
%U https://aclanthology.org/2026.starsem-conference.31/
%P 454-463

Markdown (Informal)

[The Frequency Confound in Language-Model Surprisal and Metaphor Novelty](https://aclanthology.org/2026.starsem-conference.31/) (Momen & Zarrieß, *SEM 2026)

• The Frequency Confound in Language-Model Surprisal and Metaphor Novelty (Momen & Zarrieß, *SEM 2026)

ACL

• Omar Momen and Sina Zarrieß. 2026. The Frequency Confound in Language-Model Surprisal and Metaphor Novelty. In Proceedings of the 15th Joint Conference on Lexical and Computational Semantics (*SEM 2026), pages 454–463, San Diego, California, United States. Association for Computational Linguistics.