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Summary of Rakutenai-7b: Extending Large Language Models For Japanese, by Rakuten Group Inc. et al.


RakutenAI-7B: Extending Large Language Models for Japanese

by Rakuten Group Inc., Aaron Levine, Connie Huang, Chenguang Wang, Eduardo Batista, Ewa Szymanska, Hongyi Ding, Hou Wei Chou, Jean-François Pessiot, Johanes Effendi, Justin Chiu, Kai Torben Ohlhus, Karan Chopra, Keiji Shinzato, Koji Murakami, Lee Xiong, Lei Chen, Maki Kubota, Maksim Tkachenko, Miroku Lee, Naoki Takahashi, Prathyusha Jwalapuram, Ryutaro Tatsushima, Saurabh Jain, Sunil Kumar Yadav, Ting Cai, Wei-Te Chen, Yandi Xia, Yuki Nakayama, Yutaka Higashiyama

First submitted to arxiv on: 21 Mar 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Machine Learning (cs.LG)

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The paper introduces a suite of Japanese-oriented large language models, RakutenAI-7B, which outperforms other open-source models on Japanese Language Modeling Harness benchmarks. The suite includes foundation and instruction- as well as chat-tuned models, released under the Apache 2.0 license.
Low GrooveSquid.com (original content) Low Difficulty Summary
RakutenAI-7B is a set of language models designed for Japanese text processing. It’s like having a super smart friend who can understand and generate Japanese texts. The team created this model to help improve natural language processing in Japan.

Keywords

* Artificial intelligence  * Natural language processing