llm-jp-eval Benchmark

A llm-jp-eval Benchmark is a unilingual LLM benchmark (that evaluates the performance of language models) on Japanese language tasks.

• Context:
  • It can (typically) include a range of tasks such as natural language understanding, natural language inference, text classification, named entity recognition, and machine translation between Japanese and other languages.
  • It can (often) be designed to measure how well a language model can handle the linguistic nuances and complexities unique to the Japanese language, including its writing systems (Hiragana, Katakana, and Kanji), politeness levels, and contextual understanding.
  • It can provide valuable insights into a model's ability to process and generate Japanese text, serving as a crucial tool for developers aiming to create or improve AI applications targeted at the Japanese market.
  • It can be used by researchers and practitioners to benchmark their models against established standards, facilitating comparisons and highlighting areas for improvement.
  • ...
• Example(s):
  • ...
• Counter-Example(s):
  • A dataset designed for image recognition tasks.
• See: Language Model, Natural Language Processing, Benchmarking, Japanese Language.

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References

2024

• https://llm-jp.github.io/awesome-japanese-llm/README_en.html
  • Here are seven key points about the llm-jp-eval benchmark:
    1. llm-jp-eval is a tool for automatically evaluating large-scale Japanese language models across multiple datasets, converting existing Japanese evaluation data into datasets for text generation tasks.
    2. It supports running evaluations across multiple datasets, generating instruction data (jaster) in the same format as the evaluation data prompts, and provides details on data formats and supported datasets in the DATASET.md file.
    3. Pre-processing scripts are provided to download and prepare the evaluation and instruction datasets. Evaluation is run using a config file managed with Hydra.
    4. Evaluation results and output are saved as JSON, and can be synced with Weights & Biases (W&B) for tracking. Options allow customizing model settings, data subsets, prompt templates, and generation parameters.
    5. New datasets can be added by creating a new class in the src/llm_jp_eval/datasets directory and updating a few other files. Datasets are split into train/dev/test sets following predefined criteria based on size.
    6. The tool is distributed under the Apache License 2.0, with dataset licenses listed in DATASET.md. Contributors can report issues or submit pull requests to the dev branch.
    7. An important caveat is that models instruction-tuned on the jaster data can achieve very high llm-jp-eval scores even without using the test set for tuning, so high scores alone don't necessarily indicate superior performance vs other LLMs.

2023

• https://github.com/llm-jp/llm-jp-eval
  • NOTES: Here are seven key points about the llm-jp-eval benchmark:
    1. llm-jp-eval is a tool for automatically evaluating large-scale Japanese language models across multiple datasets, converting existing Japanese evaluation data into datasets for text generation tasks.
    2. It supports running evaluations across multiple datasets, generating instruction data (jaster) in the same format as the evaluation data prompts, and provides details on data formats and supported datasets in the DATASET.md file.
    3. Pre-processing scripts are provided to download and prepare the evaluation and instruction datasets. Evaluation is run using a config file managed with Hydra.
    4. Evaluation results and output are saved as JSON, and can be synced with Weights & Biases (W&B) for tracking. Options allow customizing model settings, data subsets, prompt templates, and generation parameters.
    5. New datasets can be added by creating a new class in the src/llm_jp_eval/datasets directory and updating a few other files. Datasets are split into train/dev/test sets following predefined criteria based on size.
    6. The tool is distributed under the Apache License 2.0, with dataset licenses listed in DATASET.md. Contributors can report issues or submit pull requests to the dev branch.
    7. An important caveat is that models instruction-tuned on the jaster data can achieve very high llm-jp-eval scores even without using the test set for tuning, so high scores alone don't necessarily indicate superior performance vs other LLMs.

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