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ALL 4 health 2024 : The First Workshop on Applying LLMs in LMICs for Healthcare Solutions

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Link: https://www.nivi.io/all4health
 
When Jun 3, 2024 - Jun 3, 2024
Where Orlando, Florida, USA
Submission Deadline Mar 1, 2024
Notification Due Mar 21, 2024
Final Version Due Mar 30, 2024
Categories    NLP   computational linguistics   artificial intelligene
 

Call For Papers

We invite you to submit your research and perspectives to ALL 4 Health 2024 – The First Workshop on Applying LLMs in LMICs for Healthcare Solutions.

Submission Deadline: March 1st, 2024

Website: https://www.nivi.io/all4health

Contact: all-4-health@googlegroups.com

ALL 4 health will be held at the University of Florida on June 3rd in conjunction with the IEEE International Conference on Healthcare Informatics (ICHI 2024). There has been substantial and growing interest and funding from the development sector in applying Large Language Model (LLM) technologies in Low- and Middle-Income Countries (LMICs) to address healthcare and other social good challenges.[1] Simultaneously, there have been acknowledgements from the software industry and from NLP researchers that state of the art LLMs are heavily influenced by Western / developed world data and have significant capability gaps between high- and low-resource languages.[2,3,4] Additional research and collaboration is required to bridge this gap.

The goal of this workshop is to bring together researchers and practitioners from diverse disciplinary backgrounds to discuss challenges and opportunities for applying LLMs for health applications in low-resource settings, and to share findings on gaps, pitfalls, best practices, and opportunities for impact.

We invite novel approaches, works in progress, comparative analyses of tools, and advancing state-of-the-art work relevant to applying LLMs for health applications in low-resource languages and settings. Specific topics of interest include, but are not limited to:

* Evaluations of LLMs in contexts with substantial code-switching

* Comparisons of LLM accuracy/suitability between high- and low-resource languages

* Approaches to localizing the health information processing of LLMs in the context of the laws, culture, service availability, and public health realities in specific LMICs

* Data sources for training or tuning LLMs for use on low-resource languages or in LMIC contexts

* Studies demonstrating the health or health knowledge impact of LLM applications in low-resource language and/or LMIC contexts

* Equity- and Diversity-based evaluations of LLM performance on health domain tasks

* Evidence-based position papers on best practices

We will accept full papers (4-6 pages, including references) and abstracts (2 pages, including references). Full papers will be eligible for a Best Paper Award with a $300 (USD) prize sponsored by MSD for Mothers.

Please see https://www.nivi.io/all4health for further information including submission instructions.

Best wishes,

The ALL 4 Health organizing committee

all-4-health@googlegroups.com

https://www.nivi.io/all4health

References:

R. Shrivastava. “Gates Foundation Funds Nearly 50 Generative AI Projects In Low And Middle Income Countries.” Forbes, 10 August 2023, https://www.forbes.com/sites/rashishrivastava/2023/08/10/gates-foundation-funds-nearly-50-generative-ai-projects-in-low-and-middle-income-countries/

Viet Dac Lai, et al. "Chatgpt beyond english: Towards a comprehensive evaluation of large language models in multilingual learning." arXiv preprint arXiv:2304.05613 (2023).

J. Dodge, et al. "Documenting large webtext corpora: A case study on the colossal clean crawled corpus." arXiv preprint arXiv:2104.08758 (2021).

N.R. Robertson, et al. "ChatGPT MT: Competitive for High- (but not Low-) Resource Languages." arXiv preprint arxiv:2309.07423 (2023).


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