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Cognitively-Inspired Language Models for Investigating Human Language Acquisition and Processing

Objective

The CLIMB project aims to develop Language Models that are more cognitively grounded, with the goal of investigating the mechanisms underlying human language acquisition and processing. Large Language Models demonstrate near “superhuman” performance, but require equally “superhuman” amounts of training data and ignore key aspects of human design, such as spoken and visual input, social interaction, and cognitively grounded inductive biases. This misalignment limits their scientific relevance as models of language learning and their suitability as experimental testbeds for linguistic theories.

CLIMB introduces an integrated framework that draws on findings from linguistics, cognitive science, and developmental psychology. It will construct Language Models for multiple languages trained on ecologically valid, multimodal datasets structured by developmental stage, and with a theory-driven design including inductive biases informed by linguistic and cognitive research. These models will be tested on new challenging benchmarks enriched with behavioural and biometric data, and will serve as controlled experimental platforms for validating hypotheses about the structures of language, learning trajectories, and domain interactions, such as the interplay between syntactic and semantic processing. Interpretability analyses will track the emergence of the models’ representations and establish an explanatory framework to link them to linguistic theory.

CLIMB also contributes to artificial intelligence by demonstrating how a linguistically and cognitively motivated design can enhance efficiency, representational interpretability, and behavioural transparency. By focusing on smaller and accessible models, CLIMB will support the development of more sustainable, open language technologies. The project provides a rigorous framework for evaluating the linguistic validity of artificial learners, bridging the gap between computational modelling and human cognition.

Fields of science (EuroSciVoc)

CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.

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Keywords

Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)

Programme(s)

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Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) ERC-2025-ADG

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Host institution

UNIVERSITA DI PISA
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 2 476 572,00
Address
LUNGARNO PACINOTTI 43/44
56126 Pisa
Italy

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Region
Centro (IT) Toscana Pisa
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

No data

Beneficiaries (1)

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