Ramblings of a Curiosity Infested Mind

Namaste 🙏

I am a research scientist at Rakuten based in Singapore.

Also, check out poetry and reflections for peep into other side of me.

news

Jul 15, 2025	Rakuten Group Inc. selected for Ministry of Economy, Trade and Industry (METI) and NEDO’s GENIAC project to bolster GenAI development in Japan.
Feb 25, 2025	Release of Rakuten AI 2.0 - Mixture of Experts Model and 1.5B Small Language Model
Mar 21, 2024	Rakuten AI 7B Models Released
Jun 07, 2019	Joining Machine Translation Team at Rakuten Institute of Technology, Singapore

selected publications

arXiv

RakutenAI-7B: Extending Large Language Models for Japanese

Rakuten Group, Inc., Aaron Levine, Connie Huang, and 27 more authors

2024

Abs Bib

We introduce RakutenAI-7B, a suite of Japanese-oriented large language models that achieve the best performance on the Japanese LM Harness benchmarks among the open 7B models. Along with the foundation model, we release instruction- and chat-tuned models, RakutenAI-7B-instruct and RakutenAI-7B-chat respectively, under the Apache 2.0 license.

@misc{rakutengroup2024rakutenai7b,
  title = {RakutenAI-7B: Extending Large Language Models for Japanese},
  author = {{Rakuten Group, Inc.} and Levine, Aaron and Huang, Connie and Wang, Chenguang and Batista, Eduardo and Szymanska, Ewa and Ding, Hongyi and Chou, Hou Wei and Pessiot, Jean-François and Effendi, Johanes and Chiu, Justin and Ohlhus, Kai Torben and Chopra, Karan and Shinzato, Keiji and Murakami, Koji and Xiong, Lee and Chen, Lei and Kubota, Maki and Tkachenko, Maksim and Lee, Miroku and Takahashi, Naoki and Jwalapuram, Prathyusha and Tatsushima, Ryutaro and Jain, Saurabh and Yadav, Sunil Kumar and Cai, Ting and Chen, Wei-Te and Xia, Yandi and Nakayama, Yuki and Higashiyama, Yutaka},
  year = {2024},
  eprint = {2403.15484},
  publisher = {arXiv},
  url = {https://arxiv.org/abs/2403.15484},
  archiveprefix = {arXiv},
  primaryclass = {cs.CL},
}

ACL
Rakuten’s Participation in WAT 2021: Examining the Effectiveness of Pre-trained Models for Multilingual and Multimodal Machine Translation

Raymond Hendy Susanto, Dongzhe Wang, Sunil Yadav, and 2 more authors

In Proceedings of the 8th Workshop on Asian Translation (WAT2021), Aug 2021

Abs DOI Bib

This paper introduces our neural machine translation systems’ participation in the WAT 2021 shared translation tasks (team ID: sakura). We participated in the (i) NICT-SAP, (ii) Japanese-English multimodal translation, (iii) Multilingual Indic, and (iv) Myanmar-English translation tasks. Multilingual approaches such as mBART (Liu et al., 2020) are capable of pre-training a complete, multilingual sequence-to-sequence model through denoising objectives, making it a great starting point for building multilingual translation systems. Our main focus in this work is to investigate the effectiveness of multilingual finetuning on such a multilingual language model on various translation tasks, including low-resource, multimodal, and mixed-domain translation. We further explore a multimodal approach based on universal visual representation (Zhang et al., 2019) and compare its performance against a unimodal approach based on mBART alone.
@inproceedings{susanto-etal-2021-rakutens, title = {Rakuten{'}s Participation in {WAT} 2021: Examining the Effectiveness of Pre-trained Models for Multilingual and Multimodal Machine Translation}, author = {Susanto, Raymond Hendy and Wang, Dongzhe and Yadav, Sunil and Jain, Mausam and Htun, Ohnmar}, editor = {Nakazawa, Toshiaki and Nakayama, Hideki and Goto, Isao and Mino, Hideya and Ding, Chenchen and Dabre, Raj and Kunchukuttan, Anoop and Higashiyama, Shohei and Manabe, Hiroshi and Pa, Win Pa and Parida, Shantipriya and Bojar, Ond{\v{r}}ej and Chu, Chenhui and Eriguchi, Akiko and Abe, Kaori and Oda, Yusuke and Sudoh, Katsuhito and Kurohashi, Sadao and Bhattacharyya, Pushpak}, booktitle = {Proceedings of the 8th Workshop on Asian Translation (WAT2021)}, month = aug, year = {2021}, address = {Online}, publisher = {Association for Computational Linguistics}, url = {https://aclanthology.org/2021.wat-1.9/}, doi = {10.18653/v1/2021.wat-1.9}, pages = {96--105}, }