dmis-lab/LIQUID
LIQUID: A Framework for List Question Anwering Dataset Generation (AAAI 2023)
This project helps researchers and data scientists working on question answering systems to automatically create large, high-quality datasets for 'list questions' (questions with multiple correct answers). You input raw, unlabeled text documents (like Wikipedia articles or PubMed papers), and it outputs a dataset of questions and their corresponding list answers. This is ideal for those needing extensive training data to improve the performance of their list question answering models.
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Use this if you need to generate a large volume of training data for question-answering models that need to identify multiple correct answers from a text, especially when human-labeled data is scarce.
Not ideal if you are looking for a pre-trained question answering model to directly answer user queries, or if you only need a small, highly curated dataset.
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Python
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Last pushed
Jun 07, 2023
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