Journey Into Innovation: Japanese Large Language Models

Since the release of OpenAI’s chatbot ChatGPT in November 2022, AI applications fueled by Large Language Models (LLMs) have exploded in popularity. Now, the average office worker has a host of AI productivity tools at their disposal to assist with drafting emails, summarizing meeting notes, generating images, etc. But how accessible are these tools for non-English speaking users? 

With over 50% of websites utilizing English as their main content language, English is the predominant language for training LLMs. As these LLMs absorb massive amounts of English content, a large gap forms in precision and accessibility with foreign languages. In order to overcome this English bias, companies are racing to develop LLMs specifically designed for non-English usage. 

On May 1, 6:00pm (In-person); 6:15pm PT // May 2, 10:15 am JT (Online), the SVJP benkyokai community delves into the potential of these developing LLMs, particularly in Japanese. Guiding us through the discussion will be Tatsunori Hashimoto, Assistant Professor of Computer Science at Stanford University, and Kojin Oshiba, Co-Founder of Robust Intelligence.

SPEAKERS

Tatsunori Hashimoto: 
Tatsunori Hashimoto is an Assistant Professor in the Computer Science Department at Stanford University. His work studies algorithms and methods to make large language models and machine learning systems more trustworthy and robust. He has been recognized as a Kavli fellow of the National Academy of Sciences and a Sony research award winner, and his work has received best paper awards at ICML and CHI. Before becoming an Assistant Professor, he received a Ph.D. in computer science from MIT and his bachelor’s in statistics from Harvard University.

Kojin Oshiba: 
Kojin is a co-founder of Robust Intelligence, a San Francisco-based startup developing a product to proactively mitigate AI risk. The company has raised a total of $60M led by Sequoia Capital and Tiger Global, and is trusted by leading organizations including JPMorgan Chase, the U.S. Department of Defense, Deloitte, Tokio Marine, NEC and ZHD. Kojin received his BA in Computer Science from Harvard. He has also written multiple papers on robust machine learning accepted to top AI conferences. Kojin was named to the Forbes 30 Under 30 in 2023.