MDCure: A Scalable Pipeline for Multi-Document Instruction-Following

• URL: http://arxiv.org/abs/2410.23463v2
• Date: Wed, 13 Nov 2024 19:34:22 GMT
• Title: MDCure: A Scalable Pipeline for Multi-Document Instruction-Following
• Authors: Gabrielle Kaili-May Liu, Bowen Shi, Avi Caciularu, Idan Szpektor, Arman Cohan,
• Abstract summary: We introduce MDCure, a scalable and effective fine-tuning pipeline to enhance the MD capabilities of LLMs. MDCure is based on generation of high-quality synthetic MD instruction data from sets of related articles via targeted prompts. We also introduce MDCureRM, a multi-objective reward model which filters generated data based on their training utility for MD settings.
• Score: 40.201087646516335
• License:
• Abstract: Multi-document (MD) processing is crucial for LLMs to handle real-world tasks such as summarization and question-answering across large sets of documents. While LLMs have improved at processing long inputs, MD contexts still present challenges, such as managing inter-document dependencies, redundancy, and incoherent structures. We introduce MDCure, a scalable and effective fine-tuning pipeline to enhance the MD capabilities of LLMs without the computational cost of pre-training or reliance on human annotated data. MDCure is based on generation of high-quality synthetic MD instruction data from sets of related articles via targeted prompts. We further introduce MDCureRM, a multi-objective reward model which filters generated data based on their training utility for MD settings. With MDCure, we fine-tune a variety of LLMs, from the FlanT5, Qwen2, and LLAMA3.1 model families, up to 70B parameters in size. 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%. Our code, datasets, and models are available at https://github.com/yale-nlp/MDCure.

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