yale-nlp/MDCure-72k

---
language:
- en
license: apache-2.0
dataset_info:
features:
- name: instruction
dtype: string
- name: answer
dtype: string
- name: score
dtype: float64
splits:
- name: train
num_bytes: 797782722
num_examples: 72000
download_size: 461012455
dataset_size: 797782722
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
size_categories:
- 10K<n<100K
task_categories:
- question-answering
- summarization
- text2text-generation
- text-generation
tags:
- multi-document
- instruction-tuning
- instruction-following
---
# MDCure-72k

[📄 Paper](https://arxiv.org/pdf/2410.23463) | [🤗 HF Collection](https://huggingface.co/collections/yale-nlp/mdcure-6724914875e87f41e5445395) | [⚙️ GitHub Repo](https://github.com/yale-nlp/MDCure)

## Introduction

**MDCure** is an effective and scalable procedure for generating high-quality multi-document (MD) instruction tuning data to improve MD capabilities of LLMs. Using MDCure, we construct a suite of MD instruction datasets complementary to collections such as [FLAN](https://github.com/google-research/FLAN) and fine-tune a variety of already instruction-tuned LLMs from the FlanT5, Qwen2, and LLAMA3.1 model families, up to 70B parameters in size. We additionally introduce **MDCureRM**, an evaluator model specifically designed for the MD setting to filter and select high-quality MD instruction data in a cost-effective, RM-as-a-judge fashion. Extensive evaluations on a wide range of MD and long-context benchmarks spanning various tasks show MDCure consistently improves performance over pre-trained baselines and over corresponding base models by up to 75.5%.

We release MDCure datasets of size 12k, 36k, and 72k. We also release MDCureRM and the best MDCure'd model for each architecture/size combination. To access all our models and datasets, please visit our [HF Collection](https://huggingface.co/collections/yale-nlp/mdcure-6724914875e87f41e5445395). For further details regarding dataset construction, please see our [paper](https://arxiv.org/pdf/2410.23463) and [Github repo](https://github.com/yale-nlp/MDCure). For additional details regarding how to use **yale-nlp/MDCure-FlanT5-Base**, please see below.

<p align="center">
<img src="fig1.png" width="75%">
</p>
<p align="center" style="margin-top: 0; padding-top: 0;">
<em>The MDCure pipeline generates diverse multi-document instructions, filters them via fine-grained scoring by MDCureRM, and tunes a base LLM to enhance its multi-document capabilities.</em>
</p>

## Dataset Details

**MDCure-72k** is an open-sourced dataset aimed at improving the multi-document instruction-following ability of LLMs. It consists of 12,000 multi-document instruction-answer pairs, where each instruction input contains 2 or more related documents from the [NewSHead](https://github.com/google-research-datasets/NewSHead) dataset followed by a multi-document question or prompt concerning the context documents. Each question or prompt additionally includes a brief sentence or phrase indicating the expected length of the answer, and each answer is a text of adhering to the specified length that provides a suitable response to the question or prompt.

The dataset is provided in parquet format and contains only training data. Each data sample contains the following attributes:

```
{
"instruction": [string] The input source documents and associated question or prompt,
followed by a brief direction regarding expected output length,
"answer": [string] The response to the instruction input,
"score": [float] The score issued by MDCureRM to the instruction-answer pair,
}
```

Following the MDCure pipeline, all questions/prompts and answers were generated using GPT-3.5-Turbo and subsequently scored and filtered using [**MDCureRM**](https://huggingface.co/yale-nlp/MDCureRM) to obtain the final high-quality instruction set culminating in MDCure-72k.

## Quickstart

You can download and use the **MDCure-72k** dataset via HF Datasets as follows:

```python
from datasets import load_dataset

dataset = load_dataset("yale-nlp/MDCure-72k")

# print the first training example
print(dataset["train"][0])
```

## All MDCure Models
We open-source our custom multi-document instruction scoring model, MDCureRM, as well as our best MDCure'd models at the following links:

| Model | Huggingface Repo | Description |
|---------------------------|---------------------|------------------------------|
| **MDCureRM** | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCureRM) | Multi-objective reward model to score and filter MD instruction data more cheaply and effectively than GPT-3.5-Turbo |
| **MDCure-FlanT5-Base** | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-FlanT5-Base) | **FlanT5-Base** fine-tuned with MDCure-72k |
| **MDCure-FlanT5-Large** | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-FlanT5-Large) | **FlanT5-Large** fine-tuned with MDCure-72k |
| **MDCure-Qwen2-1.5B-Instruct** | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-Qwen2-1.5B-Instruct) | **Qwen2-1.5B-Instruct** fine-tuned with MDCure-72k |
| **MDCure-Qwen2-7B-Instruct** | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-Qwen2-7B-Instruct) | **Qwen2-7B-Instruct** fine-tuned with MDCure-72k |
| **MDCure-LLAMA3.1-8B-Instruct** | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-LLAMA3.1-8B-Instruct) | **LLAMA3.1-8B-Instruct** fine-tuned with MDCure-72k |
| **MDCure-LLAMA3.1-70B-Instruct** | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-LLAMA3.1-70B-Instruct) | **LLAMA3.1-70B-Instruct** fine-tuned with MDCure-72 |

## Citation

If you find our work useful, please cite our paper as:

```bibtex
@article{liu2024mdcure,
title={MDCure: A Scalable Pipeline for Multi-Document Instruction-Following},
author={Gabrielle Kaili-May Liu and Bowen Shi and Avi Caciularu and Idan Szpektor and Arman Cohan},
journal={arXiv preprint arXiv:2410.23463},
year={2024},
url={https://arxiv.org/abs/2410.23463}
}
```