Séminaire au DIC: «LLMs: Indication or Representation?» par Anders Sogaard

    Séminaire ayant lieu dans le cadre du doctorat en informatique cognitive, en collaboration avec le centre de recherche CRIA et l'ISC 


    Anders SOGAARD

    Jeudi le 23 novembre 2023 à 10h30

    PK-5115 (possible d'y assister à distance, pour ce faire, vous devez vous inscrire ici)   


    Titre : LLMs: Indication or Representation?



    People talk to LLMs - their new assistants, tutors, or partners - about the world they live in, but are LLMs parroting, or do they (also) have internal representations of the world? There are five popular views, it seems:

    (i)         LLMs are all syntax, no semantics.

    (ii)        LLMs have inferential semantics, no referential semantics.

    (iii)        LLMs (also) have referential semantics through picturing

    (iv)       LLMs (also) have referential semantics through causal chains.

    (v)        Only chatbots have referential semantics (through causal chains) I present three sets of experiments to suggest LLMs induce inferential and referential semantics and do so by inducing human-like representations, lending some support to view (iii). I briefly compare the representations that seem to fall out of these experiments to the representations to which others have appealed in the past.



    Anders SOGAARD is University Professor of Computer Science and Philosophy and leads the newly established Center for Philosophy of Artificial Intelligence at the University of Copenhagen. Known primarily for work on multilingual NLP, multi-task learning, and using cognitive and behavioral data to bias NLP models, Søgaard is an ERC Starting Grant and Google Focused Research Award recipient and the author of Semi-Supervised Learning and Domain Adaptation for NLP (2013), Cross-Lingual Word Embeddings (2019), and Explainable Natural Language Processing (2021). 



    Søgaard, A. (2023). Grounding the Vector Space of an Octopus. Minds and Machines 33, 33-54.

    Li, J.; et al. (2023) Large Language Models Converge on Brain-Like Representations. arXiv preprint arXiv:2306.01930

    Abdou, M.; et al. (2021) Can Language Models Encode Perceptual Structure Without Grounding? CoNLL

    Garneau, N.; et al. (2021) Analogy Training Multilingual Encoders. AAAI

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    jeudi 23 novembre 2023
    10 h 30

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    UQAM - Pavillon Président-Kennedy (PK)
    PK-5115 et en ligne
    201, avenue du Président-Kennedy
    Montréal (QC)

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