It is argued that this question cannot be answered if this question cannot be answered if a clearer distinction between language models designed as technical products and language models designed as tools for studying language itself is considered.
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.
Language models are appealing tools for research on the past. But to trust the evidence a model provides, researchers need to know whether its responses fit the period represented. Validation is challenging, because this is not a task living people ordinarily perform, and because many questions have multiple correct an...
Ted Underwood, Zi-Liang Qiu, Sarah Griebel et al.· 0 citations
This paper concludes that machines may assist translators but that they will not, by principle, be able to reach an almost perfect level and object to the huge amount of money spent on software development for systems with that objective.
Reasoning traces improve large language models (LLMs), but current models are trained to reason mostly in English. It has been shown that forcing a model to reason in another language degrades accuracy, even when the reasoning language matches the language of the prompt -- but only for a setting where the model reasons...
Oliver Hauck, Mario Sanz-Guerrero, Katharina von der Wense· 0 citations
Natural language generation (NLG) research has focused in the past on mainly two aspects: (i) the process of language generation in itself (e.g., to give theoretical insights into models of human language) and (ii) the use of such approaches to solve applied problems (e.g., generation of medical reports, weather foreca...
Eduardo Calò, Saad Mahamood· Northern European Journal of...· 0 citations
Language models frequently generate outputs in unintended languages or scripts, a phenomenon known as off-target generation. While existing research has focused on language selection, the dimension of script knowledge remains understudied: before any linguistic understanding can occur, users must recognize the graphic...
David Kletz, Sandra Mitrovic, Ljiljana Dolamic et al.· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
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