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--- |
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license: mit |
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datasets: |
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- yale-nlp/MDCure-72k |
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language: |
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- en |
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base_model: |
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- google/flan-t5-base |
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tags: |
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- multi-document |
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- long-context |
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- Long Context |
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--- |
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# MDCure-FlanT5-Base |
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[📄 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) |
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## Introduction |
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**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%. |
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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. |
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<p align="center"> |
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<img src="fig1.pdf" width="75%"> |
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</p> |
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<p align="center" style="margin-top: 0; padding-top: 0;"> |
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<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> |
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</p> |
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## Model Details |
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**yale-nlp/MDCure-FlanT5-Base** is initialized from [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) and fine-tuned on the [MDCure-72k](https://huggingface.co/datasets/yale-nlp/MDCure-72k) dataset. |
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## Requirements |
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We recommend using the latest version of HF Transformers, or any `transformers>4.35.0`, to avoid any potential versioning errors when using this model. |
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## Quickstart |
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Below we provide a code snippet demonstrating how to load the tokenizer and model and generate content in response to an input context concerning multiple source documents and a related question or instruction. We strongly recommend to separate the texts and/or instructions using `\n\n` or `<doc-sep>` to maintain consistency with the format of the data used during training. |
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```python |
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model = AutoModelForSeq2SeqLM.from_pretrained("yale-nlp/MDCure-FlanT5-Base", device_map='auto',torch_dtype="auto",) |
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tokenizer = AutoTokenizer.from_pretrained("yale-nlp/MDCure-FlanT5-Base") |
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source_text_1 = ... |
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source_text_2 = ... |
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source_text_3 = ... |
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input_text = f"{source_text_1}\n\n{source_text_2}\n\n{source_text_3}\n\nWhat happened in CHAMPAIGN regarding Lovie Smith and the 2019 defense improvements? Respond with 1-2 sentences." |
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(model.device) |
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outputs = model.generate(input_ids) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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## All MDCure Models |
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We open-source our custom multi-document instruction scoring model, MDCureRM, as well as our best MDCure'd models at the following links: |
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| Model | Huggingface Repo | Description | |
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|---------------------------|---------------------|------------------------------| |
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| **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. | |
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| **MDCure-FlanT5-Base** | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-FlanT5-Base) | **FlanT5-Base** fine-tuned with MDCure-72k | |
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| **MDCure-FlanT5-Large** | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-FlanT5-Large) | **FlanT5-Large** fine-tuned with MDCure-72k | |
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| **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 | |
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| **MDCure-Qwen2-7B-Instruct** | [🤗 HF Repo](https://huggingface.co/yale-nlp/MDCure-Qwen2-7B-Instruct) | **Qwen2-7B-Instruct** fine-tuned with MDCure-72k | |
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| **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 | |
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| **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 | |
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## Citation |
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If you find our work useful, please cite our paper as: |
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```bibtex |
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@article{liu2023mdcure, |
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title={MDCure: A Scalable Pipeline for Multi-Document Instruction-Following}, |
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author={Gabrielle Kaili-May Liu and Bowen Shi and Avi Caciularu and Idan Szpektor and Arman Cohan}, |
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journal={arXiv preprint arXiv:2410.23463}, |
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year={2024}, |
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url={https://arxiv.org/abs/2410.23463} |
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} |
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``` |
