Clean an Existing Preference Dataset with LLMs as Judges

Authored by: David Berenstein and Sara Han Díaz

• Libraries: argilla, hf-inference-endpoints
• Components: LoadDataFromDicts, UltraFeedback, KeepColumns, PreferenceToArgilla, InferenceEndpointsLLM, GlobalStep

In this tutorial, we’ll use distilabel to clean a dataset using the LLMs as judges by providing AI feedback on the quality of the data. distilabel is a synthetic data and AI feedback framework for engineers who need fast, reliable and scalable pipelines based on verified research papers. Check the documentation here.

To evaluate the responses, we will use the serverless HF Inference API integrated with distilabel. This is free but rate-limited, allowing you to test and evaluate over 150,000 public models, or your own private models, via simple HTTP requests, with fast inference hosted on Hugging Face shared infrastructure. If you need more compute power, you can deploy your own inference endpoint with Hugging Face Inference Endpoints.

Finally, to further curate the data, we will use Argilla, which allows us to provide human feedback on the data quality. Argilla is a collaboration tool for AI engineers and domain experts who need to build high-quality datasets for their projects. Check the documentation here.

Getting Started

Install the dependencies

To complete this tutorial, you need to install the distilabel SDK and a few third-party libraries via pip.

!pip install "distilabel[hf-inference-endpoints]"

!pip install "transformers~=4.0" "torch~=2.0"

Let’s make the required imports:

import random

from datasets import load_dataset

from distilabel.llms import InferenceEndpointsLLM
from distilabel.pipeline import Pipeline
from distilabel.steps import (
    KeepColumns,
    LoadDataFromDicts,
    PreferenceToArgilla,
)
from distilabel.steps.tasks import UltraFeedback

You’ll need an HF_TOKEN to use the HF Inference Endpoints. Login to use it directly within this notebook.

import os
from huggingface_hub import login

login(token=os.getenv("HF_TOKEN"), add_to_git_credential=True)

(optional) Deploy Argilla

You can skip this step or replace it with any other data evaluation tool, but the quality of your model will suffer from a lack of data quality, so we do recommend looking at your data. If you already deployed Argilla, you can skip this step. Otherwise, you can quickly deploy Argilla following this guide.

Along with that, you will need to install Argilla as a distilabel extra.

!pip install "distilabel[argilla, hf-inference-endpoints]"

The dataset

In this case, we will clean a preference dataset, so we will use the Intel/orca_dpo_pairs dataset from the Hugging Face Hub.

dataset = load_dataset("Intel/orca_dpo_pairs", split="train[:20]")

Next, we will shuffle the chosen and rejected columns to avoid any bias in the dataset.

def shuffle_and_track(chosen, rejected):
    pair = [chosen, rejected]
    random.shuffle(pair)
    order = ["chosen" if x == chosen else "rejected" for x in pair]
    return {"generations": pair, "order": order}


dataset = dataset.map(lambda x: shuffle_and_track(x["chosen"], x["rejected"]))

dataset = dataset.to_list()

(optional) Create a custom step

A step is a block in a distilabel pipeline used to manipulate, generate, or evaluate data, among other tasks. A set of predefined steps is provided, but you can also create your own custom steps. Instead of preprocessing the data as in the previous section, it is possible to use a custom step to shuffle the columns. This step should be in a separate module to be imported and used in the pipeline. In this case, the pipeline would start by loading the orca_dpo_pairs dataset using the LoadDataFromHub step and then applying the ShuffleStep.

# "shuffle_step.py"
from typing import TYPE_CHECKING, List
from distilabel.steps import GlobalStep, StepInput

if TYPE_CHECKING:
    from distilabel.steps.typing import StepOutput

import random


class ShuffleStep(GlobalStep):
    @property
    def inputs(self) -> List[str]:
        return ["instruction", "chosen", "rejected"]

    @property
    def outputs(self) -> List[str]:
        return ["instruction", "generations", "order"]

    def process(self, inputs: StepInput) -> "StepOutput":
        outputs = []

        for input in inputs:
            chosen = input["chosen"]
            rejected = input["rejected"]
            pair = [chosen, rejected]
            random.shuffle(pair)
            order = ["chosen" if x == chosen else "rejected" for x in pair]

            outputs.append({"instruction": input["instruction"], "generations": pair, "order": order})

        yield outputs

from shuffle_step import ShuffleStep

Define the pipeline

To clean an existing preference dataset, we will need to define a Pipeline with all the necessary steps. However, a similar workflow can be used to clean an SFT dataset. Below, we will go over each step in detail.

Load the dataset

We will use the dataset we just shuffled as source data.

• Component: LoadDataFromDicts
• Input columns: system, question, chosen, rejected, generations and order, the same keys as in the loaded list of dictionaries.
• Output columns: system, instruction, chosen, rejected, generations and order. We will use output_mappings to rename the columns.

load_dataset = LoadDataFromDicts(
    data=dataset[:1],
    output_mappings={"question": "instruction"},
    pipeline=Pipeline(name="showcase-pipeline"),
)
load_dataset.load()
next(load_dataset.process())

Evaluate the responses

To evaluate the quality of the responses, we will use meta-llama/Meta-Llama-3.1-70B-Instruct, applying the UltraFeedback task that judges the responses according to different dimensions (helpfulness, honesty, instruction-following, truthfulness). For an SFT dataset, you can use PrometheusEval instead.

• Component: UltraFeedback task with LLMs using InferenceEndpointsLLM
• Input columns: instruction, generations
• Output columns: ratings, rationales, distilabel_metadata, model_name

For your use case and to improve the results, you can use any other LLM of your choice.

evaluate_responses = UltraFeedback(
    aspect="overall-rating",
    llm=InferenceEndpointsLLM(
        model_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
        tokenizer_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
        generation_kwargs={"max_new_tokens": 512, "temperature": 0.7},
    ),
    pipeline=Pipeline(name="showcase-pipeline"),
)
evaluate_responses.load()
next(
    evaluate_responses.process(
        [
            {
                "instruction": "What's the capital of Spain?",
                "generations": ["Madrid", "Barcelona"],
            }
        ]
    )
)

Keep only the required columns

We will get rid of the unneeded columns.

• Component: KeepColumns
• Input columns: system, instruction, chosen, rejected, generations, ratings, rationales, distilabel_metadata and model_name
• Output columns: instruction, chosen, rejected, generations and order

keep_columns = KeepColumns(
    columns=[
        "instruction",
        "generations",
        "order",
        "ratings",
        "rationales",
        "model_name",
    ],
    pipeline=Pipeline(name="showcase-pipeline"),
)
keep_columns.load()
next(
    keep_columns.process(
        [
            {
                "system": "",
                "instruction": "What's the capital of Spain?",
                "chosen": "Madrid",
                "rejected": "Barcelona",
                "generations": ["Madrid", "Barcelona"],
                "order": ["chosen", "rejected"],
                "ratings": [5, 1],
                "rationales": ["", ""],
                "model_name": "meta-llama/Meta-Llama-3.1-70B-Instruct",
            }
        ]
    )
)

(Optional) Further data curation

You can use Argilla to further curate your data.

• Component: PreferenceToArgilla step
• Input columns: instruction, generations, generation_models, ratings
• Output columns: instruction, generations, generation_models, ratings

to_argilla = PreferenceToArgilla(
    dataset_name="cleaned-dataset",
    dataset_workspace="argilla",
    api_url="https://[your-owner-name]-[your-space-name].hf.space",
    api_key="[your-api-key]",
    num_generations=2,
)

Run the pipeline

Below, you can see the full pipeline definition:

with Pipeline(name="clean-dataset") as pipeline:

    load_dataset = LoadDataFromDicts(data=dataset, output_mappings={"question": "instruction"})

    evaluate_responses = UltraFeedback(
        aspect="overall-rating",
        llm=InferenceEndpointsLLM(
            model_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
            tokenizer_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
            generation_kwargs={"max_new_tokens": 512, "temperature": 0.7},
        ),
    )

    keep_columns = KeepColumns(
        columns=[
            "instruction",
            "generations",
            "order",
            "ratings",
            "rationales",
            "model_name",
        ]
    )

    to_argilla = PreferenceToArgilla(
        dataset_name="cleaned-dataset",
        dataset_workspace="argilla",
        api_url="https://[your-owner-name]-[your-space-name].hf.space",
        api_key="[your-api-key]",
        num_generations=2,
    )

    load_dataset.connect(evaluate_responses)
    evaluate_responses.connect(keep_columns)
    keep_columns.connect(to_argilla)

Let’s now run the pipeline and clean our preference dataset.

distiset = pipeline.run()

Let’s check it! If you have loaded the data to Argilla, you can start annotating in the Argilla UI.

You can push the dataset to the Hub for sharing with the community and embed it to explore the data.

distiset.push_to_hub("[your-owner-name]/example-cleaned-preference-dataset")

Conclusions

In this tutorial, we showcased the detailed steps to build a pipeline for cleaning a preference dataset using distilabel. However, you can customize this pipeline for your own use cases, such as cleaning an SFT dataset or adding custom steps.

We used a preference dataset as our starting point and shuffled the data to avoid any bias. Next, we evaluated the responses using a model through the serverless Hugging Face Inference API, following the UltraFeedback standards. Finally, we kept the needed columns and used Argilla for further curation.