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The Open Japanese LLM Leaderboard is a groundbreaking platform that aims to fill the knowledge gap in evaluating and comparing Japanese LLMs. With over 20 datasets, this leaderboard provides a comprehensive set of challenges for researchers to tackle, promoting collaboration and innovation in the field of natural language processing.
The Open Japanese LLM Leaderboard aims to evaluate and compare Japanese large language models (LLMs) to fill the knowledge gap in this domain. The leaderboard is a collaborative effort between Hugging Face, LLM-jp, National Institute of Informatics, and mdx program. Japanese LLMs face unique challenges due to their writing system and linguistic complexity, making robust evaluation metrics essential. Open-source Japanese LLMs are closing the gap with closed-source models, but domain-specific datasets remain a challenge. Datasets like JCommonsenseMorality have shown that Japanese researchers are developing culturally relevant NLP models. The leaderboard will continue to evolve with new evaluation tools and metrics, including Chain-of-Thought evaluation and out-of-choice rate measurement.
The field of natural language processing (NLP) has witnessed significant advancements in recent years, particularly with the development of large language models (LLMs). However, one of the major challenges in this domain is understanding how well these LLMs perform on languages other than English. This is where the Open Japanese LLM Leaderboard comes into play, a groundbreaking initiative that aims to fill this knowledge gap by providing a comprehensive platform for evaluating and comparing Japanese LLMs.
The Open Japanese LLM Leaderboard is a collaborative effort between Hugging Face and the research consortium LLM-jp, with support from the National Institute of Informatics in Tokyo, Japan, and the high-performance computing platform, mdx program. This leaderboard boasts over 20 datasets, ranging from classical NLP tasks to more modern ones, providing a diverse set of challenges for researchers to tackle.
One of the primary concerns when it comes to Japanese LLMs is their unique writing system, which combines kanjis (漢字), Hiraganas (平仮名 / ひらがな), and Katakanas (片仮名 / カタカナ). This intricate system presents a significant challenge for tokenization, making the detection of word boundaries extremely difficult. Furthermore, Japanese language exhibits a blend of Sino-Japanese, native Japanese, Latin script (romaji /ローマ字), loanwords from various languages such as Dutch, Portuguese, French, English, German, and Arabic, as well as traditional Chinese numerals. This linguistic complexity makes it essential to develop robust evaluation metrics that can accurately assess the performance of Japanese LLMs.
The Open Japanese LLM Leaderboard was inspired by the Open LLM Leaderboard, with models deployed automatically using HuggingFace's Inference endpoints and evaluated through the llm-jp-eval library on version 1.14.1. This setup allows for memory-efficient inference and serving engine vLLM on version v0.6.3, computed in the backend by the premium computer platform for research in Japan.
According to recent observations, Japanese LLMs based on open-source architectures are closing the gap with closed-source models, achieving performance similar to these more advanced models. However, domain-specific datasets such as chABSA (finance), Wikipedia Annotated Corpus (linguistic annotations), code generation (mbpp-ja), and summarization (XL-Sum) remain a challenge for most LLMs.
Interestingly, models originating from Japanese-based companies or labs have shown better scores on the JCommonsenseMorality dataset, which evaluates the model's ability to make choices according to Japanese values when faced with ethical dilemmas. This suggests that Japanese researchers are making significant strides in developing culturally relevant and context-specific NLP models.
The Open Japanese LLM Leaderboard will continue to follow the development of the evaluation tool llm-jp-eval, reflecting the constant evolution of Japanese LLMs. Future directions for this initiative include adding new datasets, such as JHumanEval (Japanese version of HumanEval) and MMLU (Measuring Massive Multitask Language Understanding). Additionally, there are plans to introduce a new evaluation system, Chain-of-Thought evaluation, which will compare the performance of LLMs when employing Chain-of-Thought prompts against basic prompts. Furthermore, a new metric support for out-of-choice rate is being explored, allowing researchers to evaluate how well each LLM can follow specific instructions.
The Open Japanese LLM Leaderboard is proudly sponsored by the National Institute of Informatics in Tokyo, Japan, in collaboration with the high-performance computing platform, mdx program. The consortium consists of several prominent researchers and institutions from across Japan, including Prof. Yusuke Miyao and Namgi Han from the University of Tokyo for their scientific consultation and guidance.
As researchers continue to push the boundaries of NLP, the Open Japanese LLM Leaderboard serves as an invaluable resource, fostering transparency in research and encouraging open-source model development philosophy. This initiative will undoubtedly play a pivotal role in promoting collaboration between Japanese and international researchers, leading to a better understanding of how well Japanese LLMs perform on various tasks.
Related Information:
https://huggingface.co/blog/leaderboard-japanese
Published: Wed Nov 20 04:20:45 2024 by llama3.2 3B Q4_K_M
