From 9b3422e0c8650961a9f49fbf4ad465c5d721f15f Mon Sep 17 00:00:00 2001 From: Kaito Sugimoto Date: Sun, 16 Jun 2024 19:32:02 +0900 Subject: [PATCH] migrate to Vitepress (#279) * migrate to Vitepress * set up workflow * try * try * fix * test * test * fix base * try * fix logo * delete logo --- .gitattributes | 3 +- ...ll-gh-pages.yml => vitepress-gh-pages.yml} | 22 +- .gitignore | 5 + .vitepress/config.mts | 51 ++ .vitepress/theme/index.ts | 16 + README.md | 44 +- build/fix_bibtex_codeblock_for_jekyll.py | 16 - README_en.md => en/README.md | 44 +- README_fr.md => fr/README.md | 44 +- package.json | 18 + yarn.lock | 799 ++++++++++++++++++ 11 files changed, 968 insertions(+), 94 deletions(-) rename .github/workflows/{jekyll-gh-pages.yml => vitepress-gh-pages.yml} (82%) create mode 100644 .gitignore create mode 100644 .vitepress/config.mts create mode 100644 .vitepress/theme/index.ts delete mode 100644 build/fix_bibtex_codeblock_for_jekyll.py rename README_en.md => en/README.md (97%) rename README_fr.md => fr/README.md (97%) create mode 100644 package.json create mode 100644 yarn.lock diff --git a/.gitattributes b/.gitattributes index c899834..afebc5a 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,2 +1 @@ -figures/scripts/*.py linguist-vendored -build/*.py linguist-vendored \ No newline at end of file +figures/scripts/*.py linguist-vendored \ No newline at end of file diff --git a/.github/workflows/jekyll-gh-pages.yml b/.github/workflows/vitepress-gh-pages.yml similarity index 82% rename from .github/workflows/jekyll-gh-pages.yml rename to .github/workflows/vitepress-gh-pages.yml index 3bccce6..ffcbcdd 100644 --- a/.github/workflows/jekyll-gh-pages.yml +++ b/.github/workflows/vitepress-gh-pages.yml @@ -1,5 +1,4 @@ -# Sample workflow for building and deploying a Jekyll site to GitHub Pages -name: Deploy Jekyll with GitHub Pages dependencies preinstalled +name: Deploy VitePress site to Pages on: # Runs on pushes targeting the default branch @@ -28,8 +27,15 @@ jobs: steps: - name: Checkout uses: actions/checkout@v3 + - name: Setup Node + uses: actions/setup-node@v4 + with: + node-version: 20 + cache: yarn - name: Setup Pages uses: actions/configure-pages@v3 + - name: Install dependencies + run: yarn install - name: Generate Contributors Image uses: jaywcjlove/github-action-contributors@v1.3.5 with: @@ -43,16 +49,12 @@ jobs: git add -A git diff --quiet && git diff --staged --quiet || git commit -m "Update Contributors Image" git push - - name: Fix Bibtex for Jekyll - run: | - python build/fix_bibtex_codeblock_for_jekyll.py - - name: Build with Jekyll - uses: actions/jekyll-build-pages@v1 - with: - source: ./ - destination: ./_site + - name: Build with VitePress + run: yarn docs:build - name: Upload artifact uses: actions/upload-pages-artifact@v2 + with: + path: .vitepress/dist # Deployment job deploy: diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..013d0d1 --- /dev/null +++ b/.gitignore @@ -0,0 +1,5 @@ +node_modules +.yarn +dist +cache +temp \ No newline at end of file diff --git a/.vitepress/config.mts b/.vitepress/config.mts new file mode 100644 index 0000000..908d5c6 --- /dev/null +++ b/.vitepress/config.mts @@ -0,0 +1,51 @@ +import { defineConfig } from 'vitepress' +import footnote from 'markdown-it-footnote' + +// https://vitepress.dev/reference/site-config +export default defineConfig({ + title: "LLM-jp", + description: "Overview of Japanese LLMs", + base: '/awesome-japanese-llm/', + themeConfig: { + socialLinks: [ + { icon: 'github', link: 'https://github.com/llm-jp/awesome-japanese-llm' } + ], + + search: { + provider: 'local' + }, + + logo: 'https://llm-jp.nii.ac.jp/assets/images/logo2.png', + + outline: { + level: [2, 4], + } + }, + rewrites: { + 'README.md': 'index.md', + 'en/README.md': 'en/index.md', + 'fr/README.md': 'fr/index.md', + }, + markdown: { + config: (md) => { + md.use(footnote) + } + }, + lastUpdated: true, + locales: { + root: { + label: '日本語', + lang: 'ja-JP', + }, + en: { + label: 'English', + lang: 'en-US', + link: '/en' + }, + fr: { + label: 'Français', + lang: 'fr-FR', + link: '/fr/' + }, + } +}) diff --git a/.vitepress/theme/index.ts b/.vitepress/theme/index.ts new file mode 100644 index 0000000..fccff97 --- /dev/null +++ b/.vitepress/theme/index.ts @@ -0,0 +1,16 @@ +// https://vitepress.dev/guide/custom-theme +import { h } from 'vue' +import type { Theme } from 'vitepress' +import DefaultTheme from 'vitepress/theme' + +export default { + extends: DefaultTheme, + Layout: () => { + return h(DefaultTheme.Layout, null, { + // https://vitepress.dev/guide/extending-default-theme#layout-slots + }) + }, + enhanceApp({ app, router, siteData }) { + // ... + } +} satisfies Theme diff --git a/README.md b/README.md index 8587601..b69c7c6 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,8 @@ # 日本語LLMまとめ -[ [**English**](./README_en.md) | [**Français**](./README_fr.md) | 日本語 ] +[ [**English**](./en/README.md) | [**Français**](./fr/README.md) | 日本語 ]

- 日本語LLM・海外LLMのパラメータサイズの推移 + 日本語LLM・海外LLMのパラメータサイズの推移

日本語LLM・海外LLMのパラメータ数の推移。日本語モデルの情報は本記事、海外モデルの情報は LifeArchitect.ai の Models table を参照しています（ただし、図のスペース上一部のモデルは省略。また、海外モデルのパラメータ数は推測値を含む）。修正・追加等ありましたらお知らせ下さい。
@@ -353,7 +353,7 @@ ### 複合型ベンチマーク -#### [Nejumi LLMリーダーボード Neo](http://nejumi.ai/) (Weights & Biases) +**[Nejumi LLMリーダーボード Neo](http://nejumi.ai/) (Weights & Biases)** 一問一答形式で言語理解を評価する [llm-jp-eval](#llm-jp-eval) とプロンプト対話で生成能力を評価する [Japanese MT-bench](#jp-mt-bench) による総合評価の結果をまとめている。 @@ -361,27 +361,27 @@ ### 基礎的な自然言語理解 (NLU) を中心に測定するベンチマーク/データセット -#### [llm-jp-eval](https://github.com/llm-jp/llm-jp-eval) (LLM-jp) +**[llm-jp-eval](https://github.com/llm-jp/llm-jp-eval) (LLM-jp)** 複数のデータセットを横断して日本語 LLM を自動評価するツールである。 対応している全データセット一覧は[こちら](https://github.com/llm-jp/llm-jp-eval/tree/main/src/llm_jp_eval/datasets)から確認できる（この中には JNLI や JCommonsenseQA といった JGLUE のタスクなども含まれている）。 評価結果は [llm-jp-eval リーダーボード](http://wandb.me/llm-jp-leaderboard) にまとめられている。 -#### [JP Language Model Evaluation Harness](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable) (Stability AI) +**[JP Language Model Evaluation Harness](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable) (Stability AI)** Stability AI による [EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) のフォーク。複数のデータセットを横断して日本語 LLM を自動評価するツールである。 対応している全データセット一覧は[こちら](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable/lm_eval/tasks/ja)から確認できる（この中には JNLI や JCommonsenseQA といった JGLUE のタスクなども含まれている）。 rinna による詳細な評価結果まとめがある: [[rinna] Benchmark of Stability-AI/lm-evaluation-harness](https://rinnakk.github.io/research/benchmarks/lm/) -#### [JGLUE](https://github.com/yahoojapan/JGLUE) (早大河原研 & ヤフー) +**[JGLUE](https://github.com/yahoojapan/JGLUE) (早大河原研 & ヤフー)** [GLUE ベンチマーク](https://gluebenchmark.com/)の日本語版として構築されたベンチマーク。MARC-ja, JCoLA, JSTS, JNLI, JSQuAD, JCommonsenseQA の 6 つのタスクを含む（[JCoLA](https://github.com/osekilab/JCoLA) は東大大関研により作成）。各タスクの詳細は[こちら](https://www.jstage.jst.go.jp/article/jnlp/30/1/30_63/_article/-char/ja)や[こちら](https://techblog.yahoo.co.jp/entry/2022122030379907/)を参照 -#### [JMMLU](https://github.com/nlp-waseda/JMMLU) (早大河原研) +**[JMMLU](https://github.com/nlp-waseda/JMMLU) (早大河原研)** [MMLU ベンチマーク](https://github.com/hendrycks/test)の日本語版として構築されたベンチマーク。自然科学・人文科学・社会科学の幅広い学術領域から 4 択問題を構成している。元の MMLU を翻訳しただけでなく、日本独自の文化的背景に基づく問題（日本問題）を新たに追加しているのが特徴である。 -#### [日本語 Open LLM Leaderboard](http://wandb.me/llm-jp-openllmleaderboard) (LLM-jp) +**[日本語 Open LLM Leaderboard](http://wandb.me/llm-jp-openllmleaderboard) (LLM-jp)** Huggingface の [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) と同様の検証を日本語 LLM に対して行ったもの。日本語 LLM の英語タスクにおける性能を確認できる。 @@ -389,48 +389,48 @@ Huggingface の [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFace ### 人間らしい応答の生成能力を中心に測定するベンチマーク/データセット -#### [Japanese MT-bench](https://github.com/Stability-AI/FastChat/tree/jp-stable/fastchat/llm_judge) (Stability AI) +**[Japanese MT-bench](https://github.com/Stability-AI/FastChat/tree/jp-stable/fastchat/llm_judge) (Stability AI)** マルチターン会話能力を問う [MT-bench](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) の日本語版。Writing, Roleplay, Reasoning, Math, Coding, Extraction, STEM, Humanities の 8 つのカテゴリから 10 問ずつ、計 80 問が収録されている。なお、日本語版作成の際には、日本の文化に合うように質問内容に一部修正が加えられている。
GPT-4 による 10 段階の絶対評価を行うスクリプトも含まれている。 -#### [Rakuda Benchmark](https://github.com/yuzu-ai/japanese-llm-ranking) (YuzuAI) +**[Rakuda Benchmark](https://github.com/yuzu-ai/japanese-llm-ranking) (YuzuAI)** 日本の地理、歴史、政治、社会に関する[40問の自由質問](https://huggingface.co/datasets/yuzuai/rakuda-questions)に対してモデルに出力を行わせる。GPT-4 が同じ質問に対する2つのモデルの出力を比べ、どちらの答えが優れているかを判断することにより、モデルのランク付けを行う。 -#### [ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100) (ELYZA) +**[ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100) (ELYZA)** 複雑な指示・タスクを含む100件の日本語データで、全てのデータに対して評価観点がアノテーションされている。
要約を修正し修正箇所を説明するタスク、具体的なエピソードから抽象的な教訓を述べるタスク、ユーザーの意図を汲み役に立つAIアシスタントとして振る舞うタスク、場合分けを必要とする複雑な算数のタスク、未知の言語からパターンを抽出し日本語訳する高度な推論を必要とするタスク、複数の指示を踏まえた上でyoutubeの対話を生成するタスク、架空の生き物や熟語に関する生成・大喜利などの想像力が求められるタスクなどが含まれている。
評価結果は[こちら](https://docs.google.com/spreadsheets/d/1mtoy4QAqDPk2f_B0vDogFoOrbA5G42DBEEHdqM4VmDI/edit#gid=1023787356)や[こちら](https://zenn.dev/elyza/articles/5e7d9373c32a98)を参照。また、より新しいモデルを含む評価結果は[こちら](https://note.com/elyza/n/n5d42686b60b7)を参照。 -#### [Japanese Vicuna QA Benchmark](https://github.com/ku-nlp/ja-vicuna-qa-benchmark) (京大 言語メディア研究室) +**[Japanese Vicuna QA Benchmark](https://github.com/ku-nlp/ja-vicuna-qa-benchmark) (京大 言語メディア研究室)** MT-Bench の前身である [vicuna-blog-eval](https://github.com/lm-sys/vicuna-blog-eval) の日本語版。一般、知識、ロールプレイ、常識、フェルミ推定、反実仮想、コーディング、数学、ライティングに関する 80 問の質問を収録している。また、GPT-4 による自動評価（勝率計算）のスクリプトも含まれている。リーダーボードは[こちら](http://wandb.me/llm-jp-vicunaleaderboard) ### 論理推論能力を測定するベンチマーク/データセット -#### [JFLD (Japanese Formal Logic Deduction)](https://aclanthology.org/2024.lrec-main.832/) (日立製作所) +**[JFLD (Japanese Formal Logic Deduction)](https://aclanthology.org/2024.lrec-main.832/) (日立製作所)** 日本語 LLM の演繹推論能力を問うデータセット（同著者らが提案している [FLD (Formal Logic Deduction)](https://github.com/hitachi-nlp/FLD) の日本語版）。LLM が持つ知識と切り分けて評価を行うために、反実仮想的なサンプルから構成されているのが特徴である。 -#### [JHumanEval](https://www.anlp.jp/proceedings/annual_meeting/2024/pdf_dir/P10-9.pdf) (日本女子大 倉光研) +**[JHumanEval](https://www.anlp.jp/proceedings/annual_meeting/2024/pdf_dir/P10-9.pdf) (日本女子大 倉光研)** 英語の指示から Python コードの生成能力を評価するベンチマークである [HumanEval](https://huggingface.co/datasets/openai_humaneval) の日本語版。日本語版を作成する際には、まず機械翻訳にかけたあと、人手での修正を行っている。 ### 特定ドメインの性能を測定するベンチマーク/データセット -#### [Japanese Language Model Financial Evaluation Harness](https://github.com/pfnet-research/japanese-lm-fin-harness) (Preferred Networks) +**[Japanese Language Model Financial Evaluation Harness](https://github.com/pfnet-research/japanese-lm-fin-harness) (Preferred Networks)** 金融分野における日本語 LLM のベンチマーク。金融分野における感情分析タスク(chabsa)、証券分析における基礎知識タスク(cma_basics)、公認会計士試験における監査に関するタスク(cpa_audit)、ファイナンシャルプランナー試験の選択肢問題のタスク(fp2)、証券外務員試験の模擬試験タスク(security_sales_1)を含む。詳細は[こちら](https://www.anlp.jp/proceedings/annual_meeting/2024/pdf_dir/C6-4.pdf)を参照 -#### [Stockmark Business Questions](https://huggingface.co/datasets/stockmark/business-questions) (ストックマーク) +**[Stockmark Business Questions](https://huggingface.co/datasets/stockmark/business-questions) (ストックマーク)** 市場動向、時事問題、社会課題、ビジネストレンドなどの知識を問う問題が50題収録されている。 ### 埋め込みモデルのベンチマーク/データセット -#### [JMTEB](https://jedworkshop.github.io/JLR2024/materials/b-3.pdf) (SB Intuitions) +**[JMTEB](https://jedworkshop.github.io/JLR2024/materials/b-3.pdf) (SB Intuitions)** [MTEB](https://github.com/embeddings-benchmark/mteb)の日本語版として作成されたベンチマーク。 文書クラスタリング、文書分類、文間類似度、文ペアラベル予測、文書抽出の5種類のタスクから構成されている（その後、リランキングタスクが新たに追加）。 @@ -438,19 +438,19 @@ MT-Bench の前身である [vicuna-blog-eval](https://github.com/lm-sys/vicuna- ### 視覚言語モデル (Vision-Language Models) のベンチマーク/データセット -#### [Heron-Bench](https://huggingface.co/datasets/turing-motors/Japanese-Heron-Bench) (Turing) +**[Heron-Bench](https://huggingface.co/datasets/turing-motors/Japanese-Heron-Bench) (Turing)** 21 枚の画像に対して計 102 問の質問が割り当てられている。日本に関する知識を要求する画像・質問になっているのが特徴である。 -#### [JA-VLM-Bench-In-the-Wild](https://huggingface.co/datasets/SakanaAI/JA-VLM-Bench-In-the-Wild) (Sakana AI) +**[JA-VLM-Bench-In-the-Wild](https://huggingface.co/datasets/SakanaAI/JA-VLM-Bench-In-the-Wild) (Sakana AI)** Sakana AI が EvoVLM-JP-v1-7B の評価のために独自に用意したデータセット。42 枚の画像に対して計 50 問の質問が割り当てられている。日本に関する知識を要求する画像・質問になっているのが特徴である。 -#### [LLaVA-Bench-In-the-Wild (Japanese)](https://github.com/turingmotors/heron/tree/main/playground/data/llava-bench-in-the-wild) (Turing) +**[LLaVA-Bench-In-the-Wild (Japanese)](https://github.com/turingmotors/heron/tree/main/playground/data/llava-bench-in-the-wild) (Turing)** [LLaVA-Bench-In-the-Wild](https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild) を DeepL で日本語に訳したもの。24 枚の画像に対して計 60 問の質問が割り当てられている。 -#### [LLaVA-Bench (COCO) Japanese](https://github.com/turingmotors/heron/tree/main/playground/data/llava-bench-ja) (Turing) +**[LLaVA-Bench (COCO) Japanese](https://github.com/turingmotors/heron/tree/main/playground/data/llava-bench-ja) (Turing)** LLaVA の評価に使われた LLaVA-Bench (COCO) データセットを DeepL で日本語に訳したもの。30 枚の画像に対して各 3 種類の質問が割り当てられている。 @@ -537,7 +537,7 @@ LLaVA の評価に使われた LLaVA-Bench (COCO) データセットを DeepL このプロジェクトに貢献してくれているコントリビューターのみなさんです！ - + diff --git a/build/fix_bibtex_codeblock_for_jekyll.py b/build/fix_bibtex_codeblock_for_jekyll.py deleted file mode 100644 index 361586c..0000000 --- a/build/fix_bibtex_codeblock_for_jekyll.py +++ /dev/null @@ -1,16 +0,0 @@ -import re -import glob - -# Get all markdown files in the current directory -file_list = glob.glob('./*.md') - -# Pattern that matches bibtex block surrounded by ``` -pattern = re.compile(r'```(.*?)```', re.DOTALL) - -for filename in file_list: - with open(filename, 'r') as file: - content = file.read() - # Replace matched pattern with {% raw %} and {% endraw %} - new_content = re.sub(pattern, '```\n{% raw %}\g<1>{% endraw %}\n```', content) - with open(filename, 'w') as file: - file.write(new_content) \ No newline at end of file diff --git a/README_en.md b/en/README.md similarity index 97% rename from README_en.md rename to en/README.md index 91044b0..f7823a1 100644 --- a/README_en.md +++ b/en/README.md @@ -1,8 +1,8 @@ # Overview of Japanese LLMs -[ English | [**Français**](./README_fr.md) | [**日本語**](./README.md) ] +[ English | [**Français**](../fr/README.md) | [**日本語**](../README.md) ]

- Parameter sizes of Japanese and non-Japanese LLMs over time + Parameter sizes of Japanese and non-Japanese LLMs over time

Evolution of parameter sizes for Japanese LLMs and non-Japanese LLMs. The information on the Japanese models is derived from this article, while the information on the non-Japanese models can be referred from the Models table on LifeArchitect.ai. However, due to space constraints in the figure, some models have been omitted. Additionally, estimates are included in the parameter count for non-Japanese models. Please notify us of any corrections, additions, or updates.
@@ -350,7 +350,7 @@ Please point out any errors on the [issues page](https://github.com/llm-jp/aweso ### Hybrid Benchmarks -#### [Nejumi LLM Leaderboard Neo](https://api.wandb.ai/links/wandb-japan/dcbaznfb) (Weights & Biases) +**[Nejumi LLM Leaderboard Neo](https://api.wandb.ai/links/wandb-japan/dcbaznfb) (Weights & Biases)** This compiles the results of a comprehensive evaluation by [llm-jp-eval](#llm-jp-eval), which evaluates language understanding in a question-and-answer format, and [Japanese MT-bench](#jp-mt-bench), which evaluates generative ability in a context of dialogue prompts. @@ -358,27 +358,27 @@ This compiles the results of a comprehensive evaluation by [llm-jp-eval](#llm-jp ### Traditional Benchmarks based on Natural Language Understanding tasks -#### [llm-jp-eval](https://github.com/llm-jp/llm-jp-eval) (LLM-jp) +**[llm-jp-eval](https://github.com/llm-jp/llm-jp-eval) (LLM-jp)** A tool that evaluates Japanese LLMs automatically across multiple datasets. The complete list of supported datasets can be found [here](https://github.com/llm-jp/llm-jp-eval/tree/main/src/llm_jp_eval/datasets) (which also includes tasks such as JNLI and JCommonsenseQA from JGLUE). Evaluation results are compiled on the [llm-jp-eval leaderboard](http://wandb.me/llm-jp-leaderboard). -#### [JP Language Model Evaluation Harness](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable) (Stability AI) +**[JP Language Model Evaluation Harness](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable) (Stability AI)** A fork by Stability AI of [EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). It is a tool for automatically evaluating Japanese LLMs across multiple datasets. The complete list of supported datasets can be found [here](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable/lm_eval/tasks/ja) (which also includes tasks such as JNLI and JCommonsenseQA from JGLUE). There is a detailed summary of the evaluation results by rinna: [[rinna] Benchmark of Stability-AI/lm-evaluation-harness](https://rinnakk.github.io/research/benchmarks/lm/) -#### [JGLUE](https://github.com/yahoojapan/JGLUE) (Waseda University Kawahara Lab and Yahoo) +**[JGLUE](https://github.com/yahoojapan/JGLUE) (Waseda University Kawahara Lab and Yahoo)** Japanese version of the [GLUE](https://gluebenchmark.com/) benchmark suite, including the MARC-ja, JCoLA, JSTS, JNLI, JSQuAD, and JCommonsenseQA tasks. [JCoLA](https://github.com/osekilab/JCoLA) is by the University of Tokyo's Oseki Lab. See [here](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.317.pdf) and [here (ja only)](https://techblog.yahoo.co.jp/entry/2022122030379907/) for further details about each task. -#### [JMMLU](https://github.com/nlp-waseda/JMMLU) (Waseda University Kawahara Lab) +**[JMMLU](https://github.com/nlp-waseda/JMMLU) (Waseda University Kawahara Lab)** A benchmark constructed as a Japanese version of the [MMLU Benchmark](https://github.com/hendrycks/test), consisting of multiple-choice questions from a wide range of academic fields including natural sciences, humanities, and social sciences. In addition to translating the original MMLU, it features newly added problems based on the unique cultural background of Japan (Japan-specific problems). -#### [Japanese Open LLM Leaderboard](http://wandb.me/llm-jp-openllmleaderboard) (LLM-jp) +**[Japanese Open LLM Leaderboard](http://wandb.me/llm-jp-openllmleaderboard) (LLM-jp)** Similar to Huggingface's [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), this leaderboard provides a verification on Japanese LLMs. You can check the performance of Japanese LLMs in English tasks. @@ -386,67 +386,67 @@ Similar to Huggingface's [Open LLM Leaderboard](https://huggingface.co/spaces/Hu ### Benchmarks on open-ended generative tasks -#### [Japanese MT-bench](https://github.com/Stability-AI/FastChat/tree/jp-stable/fastchat/llm_judge) (Stability AI) +**[Japanese MT-bench](https://github.com/Stability-AI/FastChat/tree/jp-stable/fastchat/llm_judge) (Stability AI)** The Japanese version of [MT-bench](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) asks about multi-turn conversational ability. It includes 80 questions, 10 each, from 8 categories: Writing, Roleplay, Reasoning, Math, Coding, Extraction, STEM, Humanities. Some questions have been modified to fit with Japanese culture during the production of the Japanese version. It also includes a script that performs a 10-level absolute evaluation by GPT-4. -#### [Rakuda Benchmark](https://github.com/yuzu-ai/japanese-llm-ranking) (YuzuAI) +**[Rakuda Benchmark](https://github.com/yuzu-ai/japanese-llm-ranking) (YuzuAI)** Ranking based on model answers to [40 open-ended questions](https://huggingface.co/datasets/yuzuai/rakuda-questions) on Japanese geography, history, politics, and society. Uses GPT-4 to judge model outputs pairwise, and then ranks models by fitting a Maximum Likelihood Elo/Bradley-Terry model to GPT-4's preferences. See [here](https://github.com/yuzu-ai/japanese-llm-ranking) for the data and code used to generate the ranking and [here](https://yuzuai.jp/blog/rakuda) for further explanation. -#### [ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100) (ELYZA) +**[ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100) (ELYZA)** Ranking based on model responses to [100 complex and diverse tasks](https://huggingface.co/datasets/elyza/ELYZA-tasks-100), including tasks testing summarization, correction, abstraction, induction, and other skills. Uses humans to score the model responses and then ranks models based on their mean scores. Evaluation results can be found [here](https://docs.google.com/spreadsheets/d/1mtoy4QAqDPk2f_B0vDogFoOrbA5G42DBEEHdqM4VmDI/edit#gid=1023787356) and [here](https://zenn.dev/elyza/articles/5e7d9373c32a98). For an evaluation containing newer models, see [here](https://note.com/elyza/n/n5d42686b60b7). -#### [Japanese Vicuna QA Benchmark](https://github.com/ku-nlp/ja-vicuna-qa-benchmark) (Kyoto University Language Media Processing Lab) +**[Japanese Vicuna QA Benchmark](https://github.com/ku-nlp/ja-vicuna-qa-benchmark) (Kyoto University Language Media Processing Lab)** This is the Japanese version of [vicuna-blog-eval](https://github.com/lm-sys/vicuna-blog-eval), which is the predecessor of MT-Bench. It includes 80 questions on general knowledge, role-playing, common sense, Fermi estimation, counterfactual thinking, coding, mathematics, and writing. It also includes a script for automatic evaluation by GPT-4 (win-rate calculation). The leaderboard can be found [here](http://wandb.me/llm-jp-vicunaleaderboard). ### Benchmarks for measuring logical reasoning capabilities -#### [JFLD (Japanese Formal Logic Deduction)](https://aclanthology.org/2024.lrec-main.832/) (Hitachi) +**[JFLD (Japanese Formal Logic Deduction)](https://aclanthology.org/2024.lrec-main.832/) (Hitachi)** A dataset for evaluating deductive reasoning capabilities of Japanese LLMs (the Japanese version of the [FLD (Formal Logic Deduction)](https://github.com/hitachi-nlp/FLD) proposed by the same authors). It is characterized by being composed of counterfactual samples to evaluate apart from the knowledge the LLM possesses. -#### [JHumanEval](https://huggingface.co/datasets/kogi-jwu/jhumaneval) (Japan Women's University Kuramitsu Lab) +**[JHumanEval](https://huggingface.co/datasets/kogi-jwu/jhumaneval) (Japan Women's University Kuramitsu Lab)** A Japanese version of the [HumanEval](https://huggingface.co/datasets/openai_humaneval) benchmark, which assesses the ability to generate Python code from English instructions. In creating the Japanese version, the text was first machine-translated and then manually corrected. ### Benchmarks for measuring performance in specific domains -#### [Japanese Language Model Financial Evaluation Harness](https://github.com/pfnet-research/japanese-lm-fin-harness) (Preferred Networks) +**[Japanese Language Model Financial Evaluation Harness](https://github.com/pfnet-research/japanese-lm-fin-harness) (Preferred Networks)** A benchmark for Japanese LLM in the financial sector. It includes tasks such as sentiment analysis in finance (chabsa), basic knowledge tasks in securities analysis (cma_basics), tasks related to audits in certified public accountant examinations (cpa_audit), multiple choice question tasks in financial planner exams (fp2), and mock exam tasks for securities salespeople exams (security_sales_1). For more details, please see [here](https://www.anlp.jp/proceedings/annual_meeting/2024/pdf_dir/C6-4.pdf). -#### [Stockmark Business Questions](https://huggingface.co/datasets/stockmark/business-questions) (Stockmark) +**[Stockmark Business Questions](https://huggingface.co/datasets/stockmark/business-questions) (Stockmark)** The collection includes 50 questions that probe knowledge on topics such as market trends, current affairs, social issues, and business trends. ### Benchmarks for embedding models -#### [JMTEB](https://huggingface.co/datasets/sbintuitions/JMTEB) (SB Intuitions) +**[JMTEB](https://huggingface.co/datasets/sbintuitions/JMTEB) (SB Intuitions)** A benchmark developed as the Japanese version of [MTEB](https://github.com/embeddings-benchmark/mteb). It consists of tasks such as document clustering, text classification, sentence similarity, sentence pair labeling prediction, and text extraction (a reranking task was recently added). ### Benchmarks for vision-language models -#### [Heron-Bench](https://huggingface.co/datasets/turing-motors/Japanese-Heron-Bench) (Turing) +**[Heron-Bench](https://huggingface.co/datasets/turing-motors/Japanese-Heron-Bench) (Turing)** 21 images are assigned a total of 102 questions. It is characterized by image-question pairs that require knowledge related to Japan. -#### [JA-VLM-Bench-In-the-Wild](https://huggingface.co/datasets/SakanaAI/JA-VLM-Bench-In-the-Wild) (Sakana AI) +**[JA-VLM-Bench-In-the-Wild](https://huggingface.co/datasets/SakanaAI/JA-VLM-Bench-In-the-Wild) (Sakana AI)** A dataset independently prepared by Sakana AI to evaluate EvoVLM-JP-v1-7B. It consists of 50 questions assigned to 42 images. It is characterized by images and questions that require knowledge about Japan. -#### [LLaVA-Bench-In-the-Wild (Japanese)](https://github.com/turingmotors/heron/tree/main/playground/data/llava-bench-in-the-wild) (Turing) +**[LLaVA-Bench-In-the-Wild (Japanese)](https://github.com/turingmotors/heron/tree/main/playground/data/llava-bench-in-the-wild) (Turing)** This is the Japanese version of [LLaVA-Bench-In-the-Wild](https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild), translated using DeepL. It consists of 60 questions assigned to 24 images. -#### [LLaVA-Bench (COCO) Japanese](https://github.com/turingmotors/heron/tree/main/playground/data/llava-bench-ja) (Turing) +**[LLaVA-Bench (COCO) Japanese](https://github.com/turingmotors/heron/tree/main/playground/data/llava-bench-ja) (Turing)** This is the Japanese version, translated by DeepL, of the LLaVA-Bench (COCO) dataset used to evaluate LLaVA. It consists of 30 images, each with 3 types of questions assigned to them. @@ -533,7 +533,7 @@ This is the Japanese version, translated by DeepL, of the LLaVA-Bench (COCO) dat We love contributors! Feel free to contribute to this project. - + diff --git a/README_fr.md b/fr/README.md similarity index 97% rename from README_fr.md rename to fr/README.md index 55dafd2..81fd63a 100644 --- a/README_fr.md +++ b/fr/README.md @@ -1,8 +1,8 @@ # Aperçu des grands modèles de langage (LLM) en japonais -[ [**English**](./README_en.md) | Français | [**日本語**](./README.md) ] +[ [**English**](../en/README.md) | Français | [**日本語**](../README.md) ]

- LLMs en japonais et en non-japonais par taille de paramètres au fil du temps + LLMs en japonais et en non-japonais par taille de paramètres au fil du temps

Evolution du nombre de paramètres des LLMs en japonais et en non-japonais. Pour des informations sur le modèle japonais, nous nous référons à cet article, et pour le modèle non-japonais, nous nous référons au tableau des modèles sur LifeArchitect.ai (notez cependant que certains modèles ont été omis en raison de l'espace limité sur le graphique. De plus, le nombre de paramètres pour le modèle non-japonais inclut des valeurs estimées). Veuillez nous informer de toute correction ou ajout nécessaire.
@@ -352,7 +352,7 @@ N'hésitez pas à signaler les erreurs sur la page [issues](https://github.com/l ### Benchmarks hybrides -#### [Nejumi LLM Leaderboard Neo](https://api.wandb.ai/links/wandb-japan/dcbaznfb) (Weights & Biases) +**[Nejumi LLM Leaderboard Neo](https://api.wandb.ai/links/wandb-japan/dcbaznfb) (Weights & Biases)** Cela compile les résultats d'une évaluation complète par [llm-jp-eval](#llm-jp-eval), qui évalue la compréhension de la langue dans un format de questions-réponses, et [Japanese MT-bench](#jp-mt-bench), qui évalue la capacité générative dans un contexte d'invites de dialogue. @@ -360,27 +360,27 @@ Cela compile les résultats d'une évaluation complète par [llm-jp-eval](#llm-j ### Référence traditionnelle basé sur des tâches de Compréhension du langage naturel (NLU) -#### [llm-jp-eval](https://github.com/llm-jp/llm-jp-eval) (LLM-jp) +**[llm-jp-eval](https://github.com/llm-jp/llm-jp-eval) (LLM-jp)** Un outil qui évalue automatiquement les LLM japonais à travers plusieurs jeux de données. La liste complète des jeux de données pris en charge peut être trouvée [ici](https://github.com/llm-jp/llm-jp-eval/tree/main/src/llm_jp_eval/datasets) (qui comprend également des tâches telles que JNLI et JCommonsenseQA de JGLUE). Les résultats de l'évaluation sont compilés sur le [classement llm-jp-eval](http://wandb.me/llm-jp-leaderboard). -#### [JP Language Model Evaluation Harness](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable) (Stability AI) +**[JP Language Model Evaluation Harness](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable) (Stability AI)** Un fork par Stability AI de [EleutherAI/lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). Il s'agit d'un outil pour évaluer automatiquement les LLM japonais à travers plusieurs jeux de données. La liste complète des jeux de données pris en charge peut être trouvée [ici](https://github.com/Stability-AI/lm-evaluation-harness/tree/jp-stable/lm_eval/tasks/ja) (qui comprend également des tâches telles que JNLI et JCommonsenseQA de JGLUE). Il y a un résumé détaillé des résultats de l'évaluation par rinna : [[rinna] Benchmark de Stability-AI/lm-evaluation-harness](https://rinnakk.github.io/research/benchmarks/lm/) -#### [JGLUE](https://github.com/yahoojapan/JGLUE) (Université de Waseda Laboratoire Kawahara et Yahoo) +**[JGLUE](https://github.com/yahoojapan/JGLUE) (Université de Waseda Laboratoire Kawahara et Yahoo)** Version japonais de [GLUE](https://gluebenchmark.com/) référence suite, avec les tâches MARC-ja, JCoLA, JSTS, JNLI, JSQuAD, et JCommonsenseQA. [JCoLA](https://github.com/osekilab/JCoLA) vient du laboratoire d'Oseki de l'université de Tokyo. Voir [ici](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.317.pdf) and [here (ja only)](https://techblog.yahoo.co.jp/entry/2022122030379907/) pour plus d'informations sur chaque tâches. -#### [JMMLU](https://github.com/nlp-waseda/JMMLU) (Université de Waseda Laboratoire Kawahara) +**[JMMLU](https://github.com/nlp-waseda/JMMLU) (Université de Waseda Laboratoire Kawahara)** Un benchmark construit comme une version japonaise du [MMLU Benchmark](https://github.com/hendrycks/test), consistant en des questions à choix multiples de divers domaines académiques, y compris les sciences naturelles, les humanités et les sciences sociales. En plus de la traduction du MMLU original, il contient de nouveaux problèmes basés sur le contexte culturel unique du Japon (problèmes spécifiques au Japon). -#### [Japanese Open LLM Leaderboard](http://wandb.me/llm-jp-openllmleaderboard) (LLM-jp) +**[Japanese Open LLM Leaderboard](http://wandb.me/llm-jp-openllmleaderboard) (LLM-jp)** Semblable à [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) de Huggingface, ce classement fournit une vérification sur les LLM japonais. Vous pouvez vérifier la performance des LLM japonais dans des tâches en anglais. @@ -388,67 +388,67 @@ Semblable à [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/ ### Standard des tâches génératives ouvertes -#### [Japanese MT-bench](https://github.com/Stability-AI/FastChat/tree/jp-stable/fastchat/llm_judge) (Stability AI) +**[Japanese MT-bench](https://github.com/Stability-AI/FastChat/tree/jp-stable/fastchat/llm_judge) (Stability AI)** Version japonaise du [MT-bench](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) qui interroge sur la capacité à converser en plusieurs tournures. Il inclut 80 questions, 10 de chacune des 8 catégories : écriture, jeu de rôle, raisonnement, maths, codage, extraction, STEM, sciences humaines. Certaines questions ont été modifiées pour s'adapter à la culture japonaise lors de la création de la version japonaise. Il comprend également un script qui réalise une évaluation absolue en 10 niveaux par GPT-4. -#### [Rakuda Benchmark](https://github.com/yuzu-ai/japanese-llm-ranking) (YuzuAI) +**[Rakuda Benchmark](https://github.com/yuzu-ai/japanese-llm-ranking) (YuzuAI)** Classement basé sur les réponses des modèles avec [40 questions ouvertes](https://huggingface.co/datasets/yuzuai/rakuda-questions) la géographie, l'histoire, la politique, et la société japonaise. Utilise GPT-4 pour évaluer les résultats du modèle par paires, puis classe les modèles en ajustant le maximum de vraisemblance sur le modèle de probabilité d'Elo/Bradley-Terry avec les préférences de GPT-4. Voir [ici](https://github.com/yuzu-ai/japanese-llm-ranking) pour les données et le code utilisé afin de générer le classement [ici](https://yuzuai.jp/blog/rakuda) pour obtenir davantage d'explications. -#### [ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100) (ELYZA) +**[ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100) (ELYZA)** Classement basé sur les réponses des modèles avec [100 tâches complexes et diverses](https://huggingface.co/datasets/elyza/ELYZA-tasks-100), y compris les tâches testant la synthèse, la correction, l'abstraction, l'induction et d'autres compétences. Utilise des humains pour noter les réponses du modèle, puis classe les modèles en fonction de leurs scores moyens. Les résultats d'évaluation peuvent être trouvés [ici](https://docs.google.com/spreadsheets/d/1mtoy4QAqDPk2f_B0vDogFoOrbA5G42DBEEHdqM4VmDI/edit#gid=1023787356) et [ici](https://zenn.dev/elyza/articles/5e7d9373c32a98). Pour une évaluation incluant les modèles plus récents, voir [ici](https://note.com/elyza/n/n5d42686b60b7). -#### [Japanese Vicuna QA Benchmark](https://github.com/ku-nlp/ja-vicuna-qa-benchmark) (Université de Kyoto Laboratoire de traitement des langues et des médias) +**[Japanese Vicuna QA Benchmark](https://github.com/ku-nlp/ja-vicuna-qa-benchmark) (Université de Kyoto Laboratoire de traitement des langues et des médias)** Il s'agit de la version japonaise de [vicuna-blog-eval](https://github.com/lm-sys/vicuna-blog-eval), qui est le précurseur de MT-Bench. Il comprend 80 questions sur la connaissance générale, le jeu de rôle, le bon sens, l'estimation de Fermi, la pensée contrefactuelle, le codage, les mathématiques, et l'écriture. Il comprend également un script pour une évaluation automatique par GPT-4 (calcul du taux de victoire). Le tableau de classement peut être trouvé [ici](http://wandb.me/llm-jp-vicunaleaderboard). ### Benchmarks pour mesurer les capacités de raisonnement logique -#### [JFLD (Japanese Formal Logic Deduction)](https://aclanthology.org/2024.lrec-main.832/) (Hitachi) +**[JFLD (Japanese Formal Logic Deduction)](https://aclanthology.org/2024.lrec-main.832/) (Hitachi)** Un dataset pour évaluer les capacités de raisonnement déductif des LLM japonais (la version japonaise de la [FLD (Formal Logic Deduction)](https://github.com/hitachi-nlp/FLD) proposée par les mêmes auteurs). Il se caractérise par le fait qu'il est composé d'exemples contrefactuels pour évaluer indépendamment des connaissances que possède le LLM. -#### [JHumanEval](https://huggingface.co/datasets/kogi-jwu/jhumaneval) (Université des Femmes du Japon - Laboratoire Kuramitsu) +**[JHumanEval](https://huggingface.co/datasets/kogi-jwu/jhumaneval) (Université des Femmes du Japon - Laboratoire Kuramitsu)** Une version japonaise du benchmark [HumanEval](https://huggingface.co/datasets/openai_humaneval), qui évalue la capacité à générer du code Python à partir d'instructions en anglais. En créant la version japonaise, le texte a d'abord été traduit automatiquement, puis corrigé manuellement. ### Benchmarks pour mesurer la performance dans des domaines spécifiques -#### [Japanese Language Model Financial Evaluation Harness](https://github.com/pfnet-research/japanese-lm-fin-harness) (Preferred Networks) +**[Japanese Language Model Financial Evaluation Harness](https://github.com/pfnet-research/japanese-lm-fin-harness) (Preferred Networks)** Un benchmark pour les LLM japonais dans le secteur financier. Il comprend des tâches telles que l'analyse des sentiments dans la finance (chabsa), des tâches de connaissances de base en analyse de titres (cma_basics), des tâches relatives aux audits dans les examens de comptable public certifié (cpa_audit), des tâches à questions à choix multiple dans les examens de planificateur financier (fp2), et des tâches d'examen blanc pour les examens de vendeurs de titres (security_sales_1). Pour plus de détails, veuillez consulter [ici](https://www.anlp.jp/proceedings/annual_meeting/2024/pdf_dir/C6-4.pdf). -#### [Stockmark Business Questions](https://huggingface.co/datasets/stockmark/business-questions) (Stockmark) +**[Stockmark Business Questions](https://huggingface.co/datasets/stockmark/business-questions) (Stockmark)** La collection comprend 50 questions qui approfondissent les connaissances sur des sujets tels que les tendances du marché, l'actualité, les problèmes sociaux et les tendances commerciales. ### Benchmarks pour modèles d'embeddings -#### [JMTEB](https://huggingface.co/datasets/sbintuitions/JMTEB) (SB Intuitions) +**[JMTEB](https://huggingface.co/datasets/sbintuitions/JMTEB) (SB Intuitions)** Un benchmark développé comme la version japonaise de [MTEB](https://github.com/embeddings-benchmark/mteb). Il se compose de tâches telles que le regroupement de documents, la classification de textes, la similarité de phrases, la prédiction d'étiquetage de paires de phrases et l'extraction de texte (une tâche de reclassement a été récemment ajoutée). ### Benchmarks pour modèles vision-langage -#### [Heron-Bench](https://huggingface.co/datasets/turing-motors/Japanese-Heron-Bench) (Turing) +**[Heron-Bench](https://huggingface.co/datasets/turing-motors/Japanese-Heron-Bench) (Turing)** 21 images se voient attribuer un total de 102 questions. Il est caractérisé par des paires image-question qui nécessitent une connaissance liée au Japon. -#### [JA-VLM-Bench-In-the-Wild](https://huggingface.co/datasets/SakanaAI/JA-VLM-Bench-In-the-Wild) (Sakana AI) +**[JA-VLM-Bench-In-the-Wild](https://huggingface.co/datasets/SakanaAI/JA-VLM-Bench-In-the-Wild) (Sakana AI)** Un jeu de données préparé indépendamment par Sakana AI pour évaluer EvoVLM-JP-v1-7B. Il se compose de 50 questions attribuées à 42 images. Il se caractérise par des images et des questions qui exigent une connaissance du Japon. -#### [LLaVA-Bench-In-the-Wild (Japanese)](https://github.com/turingmotors/heron/tree/main/playground/data/llava-bench-in-the-wild) (Turing) +**[LLaVA-Bench-In-the-Wild (Japanese)](https://github.com/turingmotors/heron/tree/main/playground/data/llava-bench-in-the-wild) (Turing)** Ceci est la version japonaise de [LLaVA-Bench-In-the-Wild](https://huggingface.co/datasets/liuhaotian/llava-bench-in-the-wild), traduite à l'aide de DeepL. Il se compose de 60 questions attribuées à 24 images. -#### [LLaVA-Bench (COCO) Japonais](https://github.com/turingmotors/heron/tree/main/playground/data/llava-bench-ja) (Turing) +**[LLaVA-Bench (COCO) Japonais](https://github.com/turingmotors/heron/tree/main/playground/data/llava-bench-ja) (Turing)** Il s'agit de la version japonaise, traduite par DeepL, du jeu de données LLaVA-Bench (COCO) utilisé pour évaluer LLaVA. Il se compose de 30 images, chacune avec 3 types de questions qui leur sont attribuées. @@ -533,7 +533,7 @@ Il s'agit de la version japonaise, traduite par DeepL, du jeu de données LLaVA- Nous aimons les contributeurs ! N'hésitez pas à contribuer à ce projet. - + diff --git a/package.json b/package.json new file mode 100644 index 0000000..46dc414 --- /dev/null +++ b/package.json @@ -0,0 +1,18 @@ +{ + "name": "awesome-japanese-llm", + "version": "1.0.0", + "description": "Overview of Japanese LLMs", + "main": "index.js", + "repository": "https://github.com/kaisugi/awesome-japanese-llm.git", + "author": "Kaito Sugimoto ", + "license": "Apache-2.0", + "devDependencies": { + "markdown-it-footnote": "^4.0.0", + "vitepress": "^1.2.3" + }, + "scripts": { + "docs:dev": "vitepress dev", + "docs:build": "vitepress build", + "docs:preview": "vitepress preview" + } +} diff --git a/yarn.lock b/yarn.lock new file mode 100644 index 0000000..bf6128f --- /dev/null +++ b/yarn.lock @@ -0,0 +1,799 @@ +# THIS IS AN AUTOGENERATED FILE. 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