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        <title>Representing Abstract Concepts with Images: An Investigation with Large Language Models</title>
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    <name type="personal">
        <namePart type="given">Ludovica</namePart>
        <namePart type="family">Cerini</namePart>
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    <name type="personal">
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            <title>Proceedings of the Workshop on Cognitive Aspects of the Lexicon @ LREC-COLING 2024</title>
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        <name type="personal">
            <namePart type="given">Michael</namePart>
            <namePart type="family">Zock</namePart>
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        <name type="personal">
            <namePart type="given">Emmanuele</namePart>
            <namePart type="family">Chersoni</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Yu-Yin</namePart>
            <namePart type="family">Hsu</namePart>
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            <namePart type="given">Simon</namePart>
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    <abstract>Multimodal metaphorical interpretation of abstract concepts has always been a debated problem in many research fields, including cognitive linguistics and NLP. With the dramatic improvements of Large Language Models (LLMs) and the increasing attention toward multimodal Vision-Language Models (VLMs), there has been pronounced attention on the conceptualization of abstracts. Nevertheless, a systematic scientific investigation is still lacking. This work introduces a framework designed to shed light on the indirect grounding mechanisms that anchor the meaning of abstract concepts to concrete situations (e.g. ability - a person skating), following the idea that abstracts acquire meaning from embodied and situated simulation. We assessed human and LLMs performances by a situation generation task. Moreover, we assess the figurative richness of images depicting concrete scenarios, via a text-to-image retrieval task performed on LAION-400M.</abstract>
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            <start>107</start>
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%0 Conference Proceedings
%T Representing Abstract Concepts with Images: An Investigation with Large Language Models
%A Cerini, Ludovica
%A Bondielli, Alessandro
%A Lenci, Alessandro
%Y Zock, Michael
%Y Chersoni, Emmanuele
%Y Hsu, Yu-Yin
%Y de Deyne, Simon
%S Proceedings of the Workshop on Cognitive Aspects of the Lexicon @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F cerini-etal-2024-representing
%X Multimodal metaphorical interpretation of abstract concepts has always been a debated problem in many research fields, including cognitive linguistics and NLP. With the dramatic improvements of Large Language Models (LLMs) and the increasing attention toward multimodal Vision-Language Models (VLMs), there has been pronounced attention on the conceptualization of abstracts. Nevertheless, a systematic scientific investigation is still lacking. This work introduces a framework designed to shed light on the indirect grounding mechanisms that anchor the meaning of abstract concepts to concrete situations (e.g. ability - a person skating), following the idea that abstracts acquire meaning from embodied and situated simulation. We assessed human and LLMs performances by a situation generation task. Moreover, we assess the figurative richness of images depicting concrete scenarios, via a text-to-image retrieval task performed on LAION-400M.
%U https://aclanthology.org/2024.cogalex-1.12
%P 107-113

Markdown (Informal)

[Representing Abstract Concepts with Images: An Investigation with Large Language Models](https://aclanthology.org/2024.cogalex-1.12) (Cerini et al., CogALex 2024)

• Representing Abstract Concepts with Images: An Investigation with Large Language Models (Cerini et al., CogALex 2024)

ACL

• Ludovica Cerini, Alessandro Bondielli, and Alessandro Lenci. 2024. Representing Abstract Concepts with Images: An Investigation with Large Language Models. In Proceedings of the Workshop on Cognitive Aspects of the Lexicon @ LREC-COLING 2024, pages 107–113, Torino, Italia. ELRA and ICCL.