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Evaluating and Programming Intelligent Chatbots for Any Language

Project description

Intelligent chatbots for all languages

Intelligent chatbots (ICs), such as ChatGPT, have primarily transformed content generation in English, yet there are 7 099 spoken languages globally. Research on low-resource languages (LRLs) differs from that of large labs, which focus mainly on the top 200 languages. The ERC-funded EPICAL project will, for the first time, integrate new LRLs into ICs, achieving six key advancements. These include producing accurate text without misinformation, developing cross-lingual LRL text, enhancing LRL representation through rapid post-editing and training. The project also leverages linguistic similarities among related languages for better modelling, evaluating IC knowledge of LRLs, achieving high accuracy in LRL translation, and advancing text-to-speech and automatic speech recognition for LRLs. Findings will promote minority languages and cultures in multilingual Europe.

Objective

Intelligent chatbots (ICs) such as ChatGPT have revolutionized the generation of content for a few languages such as English, but there are 7099 currently spoken languages in the world. EPICAL will, for the first time, determine how to add new low resource languages (LRLs) to ICs. We will make six advances to revolutionize the capabilities of ICs, unifying different areas of research that are incorrectly studied separately. We will: 1) determine how to generate hallucination-free text using ICs, and how to enter a virtuous cycle where LRL text is created using cross-lingual knowledge from ICs and then quickly post-edited and trained upon, resulting in a better LRL representation in the IC. 2) develop more powerful encoding and language adaptation approaches which combine the benefits of fine-tuning and adapters, taking full advantage of linguistically related languages to model LRLs. 3) enable ICs to reason about their own LRL capabilities and determine what they know and do not know. 4) unify research on machine translation and ICs to obtain ICs which can translate to LRLs with state-of-the-art accuracy. 5) enable high quality text-to-speech and automatic speech recognition of LRLs with ICs, thereby unifying the research on low resource speech processing with research on LRL text processing. 6) develop a novel evaluation methodology including a robust method for automatically measuring fact hallucination. My research group is well-known for LRL research, which differs from large commercial labs focusing only on the top 200 languages. Our work is critical for a multilingual Europe which values the role of minority languages, culture and heritage. Our innovations will benefit natural language processing beyond text generation and machine translation and strongly impact other areas of machine learning research suffering from data bottlenecks.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

(opens in new window) ERC-2023-ADG

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Host institution

TECHNISCHE UNIVERSITAET MUENCHEN
Net EU contribution
€ 2 498 200,00
Address
Arcisstrasse 21
80333 Muenchen
Germany
Region
Bayern Oberbayern München, Kreisfreie Stadt
Activity type
Higher or Secondary Education Establishments
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Total cost
€ 2 498 200,00

Beneficiaries (1)