JCrRAG: Japanese Contextual relevance RAG Benchmark

A human-annotated benchmark for evaluating Japanese Retrieval-Augmented Generation (RAG) systems, featuring multi-level complexity and diverse categories.

Benchmark Overview

JCrRAG aims to provide a comprehensive benchmark for Japanese RAG systems. It classifies questions into three complexity levels and covers a wide range of categories relevant to Japanese contexts.

Complexity Levels

Based on the framework from Zhao et al., 2024:

Level 1: Direct fact retrieval - Answers can be found directly in a single passage of the context.
Level 2: Multi-document synthesis - Answers require synthesizing information from multiple passages or documents.
Level 3: Complex reasoning and inference - Answers demand deeper reasoning, inference, or addressing hypothetical scenarios based on the context.

Categories

History - Geography - Law
Medical Care - Insurance - Finance
Internal Regulations - Japanese Culture

Evaluation Metrics

The RAG systems are evaluated on the following four dimensions:

Metric	Description
Faithfulness	Factual consistency of the generated answer with the provided context.
Relevance	Pertinence of the generated answer to the posed question.
Completeness	Coverage of all required information elements from the question in the answer.
Utilization	Effective use of contextual evidence in formulating the answer.

Getting Started

Prerequisites

Python 3.10+
pip package manager

Installation

# git clone this repo (so you have the folder jcrrageval)
cd jcrrageval
pip install -r requirements.txt

Dataset Preparation

Run the data preparation script to download the data and format input:

python prepare_data.py

Processed data will be saved to ./data/jccrag.json.

Model Evaluation

Generate answers from your RAG system using either local models with vLLM or API-based models.

Local vLLM Models

Generate answers:
```
python gen_answer.py \
   --model_path $MODEL_PATH \
   --api local \
   --tensor_parallel_size $NUM_GPUS \
   --benchmark_name $BENCHMARK_NAME
```
Script Parameters for gen_answer.py (Local vLLM):
- --model_path ($MODEL_PATH): Path to the local Hugging Face model directory or a model identifier from Hugging Face Hub that is compatible with vLLM.
- --api: Must be set to local to use vLLM.
- --tensor_parallel_size ($NUM_GPUS): The number of GPUs to use for tensor parallelism with vLLM.
- --benchmark_name ($BENCHMARK_NAME): The name of the benchmark dataset (e.g., jcrrag) that was processed by prepare_data.py. This usually corresponds to the sub-folder name in ./data/.

API-based Models

Generate answers (OpenAI-compatible API):
```
python gen_answer.py \
  --benchmark_name $BENCHMARK_NAME \
  --model_path $MODEL_NAME \
  --api api \
  --base-url $API_ENDPOINT \
  --api-key $API_KEY
```
Script Parameters for gen_answer.py (API-based):
- --benchmark_name ($BENCHMARK_NAME): The name of the benchmark dataset (e.g., jccrag) that was processed by prepare_data.py.
- --model_path ($MODEL_NAME): The model identifier/name as recognized by the API (e.g., gpt-4o, claude-3-opus-20240229).
- --api: Must be set to api to use an external API.
- --base-url ($API_ENDPOINT): The base URL of the OpenAI-compatible API endpoint.
- --api-key ($API_KEY): Your API key for authentication with the service.

Evaluation Judgement

Generate assessment scores for the generated answers using a powerful LLM as a judge (e.g., GPT-4o).

python gen_judgement.py \
   --model_list $YOUR_MODEL_NAME \
   --judge-models gpt-4o \
   --benchmark_name $BENCHMARK_NAME

Script Parameters for gen_judgement.py:

--model_list ($YOUR_MODEL_NAME): A comma-separated string of model names whose generated answers you want to evaluate. These names should correspond to the output files/folders created by gen_answer.py.
--judge-models: The model(s) to be used as the judge. Currently, gpt-4o is supported. You can specify one or more judge models, comma-separated.
--benchmark_name ($BENCHMARK_NAME): The name of the benchmark dataset (e.g., jcrrag) for which the judgements are to be generated.

Demo Results (Leaderboard)

Below are illustrative results. The table shows scores for each model as judged by two different LLMs: gpt-4o and o3-mini. Scores represent the average across the four metrics (Faithfulness, Relevance, Completeness, Utilization) for each complexity level.

Model	Judge: gpt-4o				Judge: o3-mini
	Level 1	Level 2	Level 3	Average	Level 1	Level 2	Level 3	Average
LHTM-OPT3	7.95	7.65	7.63	7.74	7.77	7.18	7.04	7.33
DeepSeek-V3	7.96	7.65	7.52	7.71	7.80	7.05	6.88	7.24
Qwen2.5-72B-Instruct	7.92	7.56	7.53	7.67	7.79	6.94	6.86	7.20
Llama-3.3-70B-Instruct	7.87	7.55	7.22	7.55	7.79	7.12	6.68	7.19
gpt-4o	7.92	7.56	7.49	7.66	7.82	6.88	6.84	7.18
gemma-3-27b-it	7.87	7.41	7.03	7.44	7.71	6.58	6.92	7.07
tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4	7.83	7.31	6.83	7.32	7.70	6.71	6.03	6.81
tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3	7.78	7.24	6.70	7.24	7.61	6.58	5.87	6.68
LHTM-OPT-2.0	7.59	6.98	5.77	6.78	7.37	6.20	4.50	6.02

Note: The 'Average' columns show the average score across all three complexity levels for that model, as evaluated by the respective judge model.

⚠️ Important Note

Evaluation via vLLM is limited to models currently supported by vLLM. You can verify compatibility at vLLM Supported Models. For models not supported by vLLM, please load them directly from Hugging Face. Additionally, for newly released models such as Qwen3 (as of May 2025), ensure you update your vLLM library to the latest version to support these models.

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