Quality > Quantity: Synthetic Corpora from Foundation Models for Closed-Domain Extractive Question Answering

• URL: http://arxiv.org/abs/2310.16995v1
• Date: Wed, 25 Oct 2023 20:48:16 GMT
• Title: Quality > Quantity: Synthetic Corpora from Foundation Models for Closed-Domain Extractive Question Answering
• Authors: Saptarshi Sengupta, Connor Heaton, Shreya Ghosh, Preslav Nakov, Prasenjit Mitra
• Abstract summary: We study extractive question answering within closed domains and introduce the concept of targeted pre-training. Our proposed framework uses Galactica to generate synthetic, targeted'' corpora that align with specific writing styles and topics.
• Score: 35.38140071573828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain adaptation, the process of training a model in one domain and applying it to another, has been extensively explored in machine learning. While training a domain-specific foundation model (FM) from scratch is an option, recent methods have focused on adapting pre-trained FMs for domain-specific tasks. However, our experiments reveal that either approach does not consistently achieve state-of-the-art (SOTA) results in the target domain. In this work, we study extractive question answering within closed domains and introduce the concept of targeted pre-training. This involves determining and generating relevant data to further pre-train our models, as opposed to the conventional philosophy of utilizing domain-specific FMs trained on a wide range of data. Our proposed framework uses Galactica to generate synthetic, ``targeted'' corpora that align with specific writing styles and topics, such as research papers and radiology reports. This process can be viewed as a form of knowledge distillation. We apply our method to two biomedical extractive question answering datasets, COVID-QA and RadQA, achieving a new benchmark on the former and demonstrating overall improvements on the latter. Code available at https://github.com/saptarshi059/CDQA-v1-Targetted-PreTraining/tree/main.

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