MaritimeBench

Overview

MaritimeBench is a benchmark for evaluating AI models on maritime-related multiple-choice questions in Chinese. It consists of specialized questions related to maritime knowledge, navigation, marine engineering, and seafaring operations.

Task Description

Task Type: Maritime Knowledge Multiple-Choice QA (Chinese)
Input: Maritime-related question with 4 answer choices (A-D)
Output: Correct answer letter in brackets [A/B/C/D]
Language: Chinese

Key Features

Specialized maritime domain questions
Chinese language evaluation
Multiple subsets covering different maritime topics
Tests professional maritime knowledge
Standardized Chinese exam format

Evaluation Notes

Default configuration uses 0-shot evaluation
Evaluates on test split
Simple accuracy metric
Answers extracted using regex for bracketed format [A/B/C/D]

Properties

Property	Value
Benchmark Name	`maritime_bench`
Dataset ID	HiDolphin/MaritimeBench
Paper	N/A
Tags	`Chinese`, `Knowledge`, `MCQ`
Metrics	`acc`
Default Shots	0-shot
Evaluation Split	`test`

Data Statistics

Metric	Value
Total Samples	1,888
Prompt Length (Mean)	259.04 chars
Prompt Length (Min/Max)	173 / 717 chars

Sample Example

Subset: default

{
  "input": [
    {
      "id": "0b8c5f63",
      "content": "请回答单选题。要求只输出选项，不输出解释，将选项放在[]里，直接输出答案。示例：\n\n题目：在船舶主推进动力装置中，传动轴系在运转中承受以下复杂的应力和负荷，但不包括______。\n选项：\nA. 电磁力\nB. 压拉应力\nC. 弯曲应力\nD. 扭应力\n答：[A]\n 当前题目\n 船舶电力系统供电网络中，放射形网络的特点是______。①发散形传输②环形传输③缺乏冗余④冗余性能好\n选项：\nA. ②③\nB. ①③\nC. ②④\nD. ①④"
    }
  ],
  "choices": [
    "②③",
    "①③",
    "②④",
    "①④"
  ],
  "target": "B",
  "id": 0,
  "group_id": 0
}

Prompt Template

Prompt Template:

请回答单选题。要求只输出选项，不输出解释，将选项放在[]里，直接输出答案。示例：

题目：在船舶主推进动力装置中，传动轴系在运转中承受以下复杂的应力和负荷，但不包括______。
选项：
A. 电磁力
B. 压拉应力
C. 弯曲应力
D. 扭应力
答：[A]
 当前题目
 {question}
选项：
{choices}

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets maritime_bench \
    --limit 10  # Remove this line for formal evaluation

Using Python

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['maritime_bench'],
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)

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