<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chatterjee-etal-2023-cabbage">
    <titleInfo>
        <title>Cabbage Sweeter than Cake? Analysing the Potential of Large Language Models for Learning Conceptual Spaces</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Usashi</namePart>
        <namePart type="family">Chatterjee</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Amit</namePart>
        <namePart type="family">Gajbhiye</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Steven</namePart>
        <namePart type="family">Schockaert</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The theory of Conceptual Spaces is an influential cognitive-linguistic framework for representing the meaning of concepts. Conceptual spaces are constructed from a set of quality dimensions, which essentially correspond to primitive perceptual features (e.g. hue or size). These quality dimensions are usually learned from human judgements, which means that applications of conceptual spaces tend to be limited to narrow domains (e.g. modelling colour or taste). Encouraged by recent findings about the ability of Large Language Models (LLMs) to learn perceptually grounded representations, we explore the potential of such models for learning conceptual spaces. Our experiments show that LLMs can indeed be used for learning meaningful representations to some extent. However, we also find that fine-tuned models of the BERT family are able to match or even outperform the largest GPT-3 model, despite being 2 to 3 orders of magnitude smaller.</abstract>
    <identifier type="citekey">chatterjee-etal-2023-cabbage</identifier>
    <identifier type="doi">10.18653/v1/2023.emnlp-main.725</identifier>
    <location>
        <url>https://aclanthology.org/2023.emnlp-main.725</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>11836</start>
            <end>11842</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Cabbage Sweeter than Cake? Analysing the Potential of Large Language Models for Learning Conceptual Spaces
%A Chatterjee, Usashi
%A Gajbhiye, Amit
%A Schockaert, Steven
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chatterjee-etal-2023-cabbage
%X The theory of Conceptual Spaces is an influential cognitive-linguistic framework for representing the meaning of concepts. Conceptual spaces are constructed from a set of quality dimensions, which essentially correspond to primitive perceptual features (e.g. hue or size). These quality dimensions are usually learned from human judgements, which means that applications of conceptual spaces tend to be limited to narrow domains (e.g. modelling colour or taste). Encouraged by recent findings about the ability of Large Language Models (LLMs) to learn perceptually grounded representations, we explore the potential of such models for learning conceptual spaces. Our experiments show that LLMs can indeed be used for learning meaningful representations to some extent. However, we also find that fine-tuned models of the BERT family are able to match or even outperform the largest GPT-3 model, despite being 2 to 3 orders of magnitude smaller.
%R 10.18653/v1/2023.emnlp-main.725
%U https://aclanthology.org/2023.emnlp-main.725
%U https://doi.org/10.18653/v1/2023.emnlp-main.725
%P 11836-11842

Markdown (Informal)

[Cabbage Sweeter than Cake? Analysing the Potential of Large Language Models for Learning Conceptual Spaces](https://aclanthology.org/2023.emnlp-main.725) (Chatterjee et al., EMNLP 2023)

• Cabbage Sweeter than Cake? Analysing the Potential of Large Language Models for Learning Conceptual Spaces (Chatterjee et al., EMNLP 2023)

ACL

• Usashi Chatterjee, Amit Gajbhiye, and Steven Schockaert. 2023. Cabbage Sweeter than Cake? Analysing the Potential of Large Language Models for Learning Conceptual Spaces. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 11836–11842, Singapore. Association for Computational Linguistics.