Augmenting an Existing Dataset

DataDreamer can help augment existing datasets using LLMs. We demonstrate this below by augmenting questions from HotpotQA with a decomposition of what steps a user would need to take to solve the complex question.

See the resulting synthetic dataset.

from datadreamer import DataDreamer
from datadreamer.llms import OpenAI
from datadreamer.steps import ProcessWithPrompt, HFHubDataSource

with DataDreamer("./output"):
    # Load GPT-4
    gpt_4 = OpenAI(model_name="gpt-4")

    # Get HotPot QA questions
    hotpot_qa_dataset = HFHubDataSource(
        "Get Hotpot QA Questions",
        "hotpot_qa",
        config_name="distractor",
        split="train",
    ).select_columns(["question"])

    # Keep only 1000 questions as a quick demo
    hotpot_qa_dataset = hotpot_qa_dataset.take(1000)

    # Ask GPT-4 to decompose the question
    questions_and_decompositions = ProcessWithPrompt(
        "Generate Decompositions",
        inputs={"inputs": hotpot_qa_dataset.output["question"]},
        args={
            "llm": gpt_4,
            "instruction": (
                "Given the question which requires multiple steps to solve, give a numbered list of intermediate questions required to solve the question."
                "Return only the list, nothing else."
            ),
        },
        outputs={"inputs": "questions", "generations": "decompositions"},
    ).select_columns(["questions", "decompositions"])

    # Publish and share the synthetic dataset
    questions_and_decompositions.publish_to_hf_hub(
        "datadreamer-dev/hotpot_qa_augmented",
    )