Project description
Advancing multilingual natural language processing technology
Large language models (LLMs), such as ChatGPT, widely adopted across various sectors, rely on natural language processing (NLP) technology to generate fluent responses from natural language instructions. However, both LLMs and other NLP-based applications have been largely limited to a few languages due to the vast amounts of linguistic data required for their training. The ERC-funded CulturAL project aims to overcome this barrier by developing an innovative methodology for the cross-lingual transfer of LLMs, enabling their adaptation to a wide range of languages, dialects, and cultural contexts.
Objective
My research project focuses on natural language processing (NLP), an area of artificial intelligence concerned with automatic interpretation and generation of human language. NLP is well-known for its widely-used applications, such as machine translation (MT), text mining, question answering or dialogue systems. Much of this technology is now powered by large language models (LLMs), such as ChatGPT, which take a natural language instruction or question as input, and generate a fluent response. Due to their impressive performance in a range of tasks and their ease of use, in less than a year they received a wide adoption in many societal contexts (education, journalism, healthcare and others), and became the central paradigm in the field of NLP.
The development of LLMs, however, requires access to a vast amount of data and resources in a given language, as well as considerable computational infrastructure. As a result, these models are in practice limited to a handful of widely-spoken languages, leaving over 6,000 of the world’s languages and dialects without access to language technology. Furthermore, research on LLM alignment, which aims to ensure the safety of their use, has been almost exclusively directed toward the English-speaking world. Taken together, these problems lead to a major inequity in today’s language technology and artificial intelligence more broadly.
Taking a step towards a more inclusive and equitable language technology, this project will develop a novel methodology for cross-lingual transfer of LLMs to a wide-range of (low-resource, understudied) languages and dialects, and their alignment to diverse cultural contexts. The project will, therefore, advance multilingual NLP technology, extending its reach to populations currently underserved by NLP and making it safe for them to use.
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.
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.
- humanities languages and literature general language studies
- natural sciences computer and information sciences artificial intelligence
- natural sciences computer and information sciences data science natural language processing
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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Topic(s)
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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.
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.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2024-COG
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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.
1012WX Amsterdam
Netherlands
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.