BioMixQA#

Overview#

BiomixQA is a curated biomedical question-answering dataset designed to evaluate AI models on biomedical knowledge and reasoning. It has been utilized to validate the Knowledge Graph based Retrieval-Augmented Generation (KG-RAG) framework across different LLMs.

Task Description#

  • Task Type: Biomedical Multiple-Choice Question Answering

  • Input: Biomedical question with multiple answer choices

  • Output: Correct answer letter

  • Domain: Biomedical sciences, healthcare, life sciences

Key Features#

  • Curated biomedical questions from diverse sources

  • Tests medical and biological knowledge comprehension

  • Validates RAG framework effectiveness for biomedical domain

  • Multiple-choice format for standardized evaluation

  • Useful for evaluating healthcare AI systems

Evaluation Notes#

  • Default configuration uses 0-shot evaluation

  • Simple accuracy metric for performance measurement

  • Evaluates on test split

  • No few-shot examples provided

Properties#

Property

Value

Benchmark Name

biomix_qa

Dataset ID

extraordinarylab/biomix-qa

Paper

N/A

Tags

Knowledge, MCQ, Medical

Metrics

acc

Default Shots

0-shot

Evaluation Split

test

Data Statistics#

Metric

Value

Total Samples

306

Prompt Length (Mean)

344.93 chars

Prompt Length (Min/Max)

316 / 393 chars

Sample Example#

Subset: default

{
  "input": [
    {
      "id": "5e830918",
      "content": "Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of A,B,C,D,E.\n\nOut of the given list, which Gene is associated with head and neck cancer and uveal melanoma.\n\nA) ABO\nB) CACNA2D1\nC) PSCA\nD) TERT\nE) SULT1B1"
    }
  ],
  "choices": [
    "ABO",
    "CACNA2D1",
    "PSCA",
    "TERT",
    "SULT1B1"
  ],
  "target": "B",
  "id": 0,
  "group_id": 0,
  "metadata": {}
}

Prompt Template#

Prompt Template:

Answer the following multiple choice question. The entire content of your response should be of the following format: 'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of {letters}.

{question}

{choices}

Usage#

Using CLI#

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

run_task(task_cfg=task_cfg)