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Featuring rapid communication capabilities, this new LLM generates answers 3 times faster than conventional LLMs, and is aimed towards contact centers and corporate help desks
TOKYO, April 29, 2024--(BUSINESS WIRE)--PKSHA Technology Inc. (TOKYO:3993) has developed one of the first Japanese-English Large Language Models (LLM) using Retentive Network (RetNet) (*1) in collaboration with Microsoft Japan Co., Ltd. Through this LLM development, PKSHA will further enhance the practicality of generative AI in the business world, primarily focusing on boosting productivity within contact centers and corporate help desks. Actual operation in business environments will begin in stages from April 2024.
Overview of PKSHA's LLM: First Japanese-English LLM using 'RetNet'
PKSHA has developed a new LLM with the following features, leveraging Azure’s AI Infrastructure and technical assistance from Microsoft Japan.
This model is the world's first (*2) Japanese-English LLM using RetNet, which is anticipated to be a leading successor to the widely-used Transformer. RetNet, developed by Microsoft Research Asia, has a fast learning speed. It also excels in inference speed and memory efficiency when processing long text input, while maintaining or exceeding the accuracy of traditional models. The superior memory efficiency means that the model can run on fewer GPUs (*3) than conventional models, making it more cost effective. This architecture enables our Japanese-English LLM to combine efficiency in long text comprehension with excellent response speed.
This is a 7B parameter model, a size that balances output accuracy and operating cost for implementation in contact centers and corporate help desk operations.
For example, when inputting the text from two pages of a Japanese newspaper (*4), this model can output a response approximately three times faster than a conventional model, without compromising on accuracy. The model's efficiency improves proportionally with the volume of input information.
The LLM has been developed using DeepSpeed (*5), a deep learning framework developed by Microsoft Research. Microsoft provided the RetNet modeling expertise and Azure’s purpose built AI Infrastructure virtual machines optimized for AI workloads, to take advantage of DeepSpeed’s strength - highly parallel and distributed processing capabilities. With RetNet and DeepSpeed, we were able to efficiently train and achieve early performance validation with a prototype model.
The benefit of instant answers will transform contact center and corporate help desk operations
Founded in 2012, PKSHA has been focusing on the research and development of natural language processing (NLP) and the implementation of AI in society, mainly in the field of communication. With a strong track record of more than 6,000 AI implementations, mainly in areas of contact centers and corporate help desks, PKSHA will promote the use of this LLM to achieve further deployment in these areas. Our model is developed on the premise that it will be implemented in the business environment based on a practical understanding of the customer's challenges.