{"id":995568,"date":"2024-01-05T08:06:54","date_gmt":"2024-01-05T16:06:54","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&p=995568"},"modified":"2024-05-29T18:20:39","modified_gmt":"2024-05-30T01:20:39","slug":"afmr-multicultural-analysis-and-empowerment","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/afmr-multicultural-analysis-and-empowerment\/","title":{"rendered":"AFMR: Multicultural Analysis and Empowerment"},"content":{"rendered":"
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Multicultural Analysis and Empowerment<\/h1>\n\n\n\n

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Academic research plays such an important role in advancing science, technology, culture, and society. This grant program helps ensure this community has access to the latest and leading AI models.<\/em><\/strong><\/p>\nBrad Smith, Vice Chair and President<\/cite><\/blockquote>\n\n\n\n

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AFMR Goal: Improve human interactions via sociotechnical research<\/h2>\n\n\n\n

which increases trust, human ingenuity, creativity, and productivity, and decreases the digital divide while reducing the risks of developing AI which does not benefit individuals and society<\/p>\n<\/div>\n\n\n\n

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The research projects primarily focus on enhancing language models, emphasizing underrepresented languages and cultures. Projects aim to improve the accuracy of health-related responses and fine-tune models for specific languages like Vietnamese and various Indian languages. Cultural intelligence is a key goal, promoting linguistic inclusivity and understanding model behavior with knowledge graph tools. Additional efforts involve developing model-editing techniques for interpretability and robustness, particularly in underrepresented languages. The overarching aim is to enhance language models for improved accuracy, adaptability, and inclusivity across diverse languages and cultures.<\/p>\n\n\n\n

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University of Waterloo<\/strong>: Jimmy Lin (PI)<\/p>\n\n\n\n

The proposal aims to build robust foundation models for African languages to bridge the technology gap affecting these communities. The project objectives include design and optimization of model architecture, multilingual transfer learning, evaluation, and making the resources openly available.<\/p>\n\n\n\n\n\n

University of British Columbia<\/strong>: Vered Shwartz (PI)<\/p>\n\n\n\n

The proposal aims at addressing the cultural bias in Large Language Models (LLMs), which currently hold a heavy Western, North American, or even US-centric lens. By constructing a new dataset consisting of narratives that evoke social norms, the proposal aims to test the values of English LLMs as they reflect in real-world scenarios and better align the responses of LLMs with the values of diverse cultures.<\/p>\n\n\n\n\n\n

Kennesaw State University<\/strong>: Dylan Goldblatt (PI)<\/p>\n\n\n\n

This project aims to explore applications of AI to provide personalized and culturally-responsive support for second language learners at KSU. The objectives are to establish whether an AI learning support approach improves performance and engagement in language courses; if the approach helps narrow the achievement and engagement gap for underprepared students; and whether the support approach is successful across various languages.<\/p>\n\n\n\n\n\n

New York University<\/strong>: Duygu Ataman (PI)<\/p>\n\n\n\n

Recent advances have brought Large Language Models (LLMs) to an important stage that will play a significant role in shaping the next generation of applications in essential social domains, such as education and the media. Despite the continuous exploration of its remarkable capabilities, the performance of state-of-the-art models in most languages typically falls short of matching their counterparts in English. This project aims to bridge this gap by developing an adaptation methodology to improve LLM compatibility with under-resourced languages. The study uses Turkic languages as a case study, whose grammatical features present a challenging yet ideal setting for assessing NLP models.<\/p>\n\n\n\n\n\n

Georgia Institute of Technology<\/strong>: Srijan Kumar (PI)<\/p>\n\n\n\n

Investigate the capabilities of GPT-4 and its effectiveness in answering health-related queries in various languages. Our research will develop a comprehensive understanding of how broadly applicable the health-related reasoning abilities of foundational models are beyond the English language.<\/p>\n\n\n\n

Related papers:<\/strong><\/p>\n\n\n\n