aiqbench analysis
This paper critically examines the ontological status of language models trained on synthetic and translated data, questioning whether they represent genuine linguistic structures or merely artifacts of their training regimes. By distinguishing between models built as technical products versus tools for linguistic study, it challenges the prevailing assumption that NLP systems must strictly adhere to natural human languages to be valid. The work offers a nuanced perspective on the future trajectory of language modeling, suggesting that diverse definitions of "language" may emerge depending on the intended application.
Language, Language Models, and What We're Talking About
arXiv:2609.03577v1 Announce Type: new Abstract: Language models are commonly discussed as technical artefacts, but they are obviously shaped by the linguistic worlds conveyed by data during their training. Using Italian language models as evidence, I want to bring attention to the nature of the systems which result from training and specialising models on translated and synthetic data, and further curating them, and to the meaning of testing them on equally unnatural data. Are these eventually models of Italian? Are they models of language? Does NLP still care about language? These questions yield another, more concrete question: what language do we actually want language models to produce? I argue that this question cannot be answered if we do not first consider a clearer distinction between language models designed as technical products and language models designed as tools for studying language itself. The answers then might be diverse, the languages we are talking about might be diverse, and the picture might not be as pessimistic as we fear.
Abstract:Language models are commonly discussed as technical artefacts, but they are obviously shaped by the linguistic worlds conveyed by data during their training. Using Italian language models as evidence, I want to bring attention to the nature of the systems which result from training and specialising models on translated and synthetic data, and further curating them, and to the meaning of testing them on equally unnatural data. Are these eventually models of Italian? Are they models of language? Does NLP still care about language? These questions yield another, more concrete question: what language do we actually want language models to produce? I argue that this question cannot be answered if we do not first consider a clearer distinction between language models designed as technical products and language models designed as tools for studying language itself. The answers then might be diverse, the languages we are talking about might be diverse, and the picture might not be as pessimistic as we fear.
Computation and Language (cs.CL)
arXiv:2609.03577 [cs.CL]
(or arXiv:2609.03577v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.03577 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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