General-QA#

Overview#

General-QA is a customizable question answering benchmark for evaluating language models on open-ended text generation tasks. It supports flexible data formats and configurable evaluation metrics.

Task Description#

  • Task Type: Open-Ended Question Answering

  • Input: Question (with optional system prompt and conversation history)

  • Output: Free-form text answer

  • Flexibility: Supports custom datasets via local files

Key Features#

  • Flexible input format (query/answer or messages format)

  • Optional system prompt support

  • BLEU and Rouge evaluation metrics

  • Custom dataset support via local file loading

  • Extensible for various QA use cases

Evaluation Notes#

  • Default configuration uses 0-shot evaluation

  • Default metrics: BLEU, Rouge (Rouge-L-R as main score)

  • Evaluates on test split

  • See User Guide for dataset format

Properties#

Property

Value

Benchmark Name

general_qa

Dataset ID

general_qa

Paper

N/A

Tags

Custom, QA

Metrics

BLEU, Rouge

Default Shots

0-shot

Evaluation Split

test

Data Statistics#

Statistics not available.

Sample Example#

Sample example not available.

Prompt Template#

Prompt Template:

请回答问题
{question}

Usage#

Using CLI#

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets general_qa \
    --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=['general_qa'],
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)