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Workshop: Instruction Tuning and Instruction Following

Releasing the CRaQAn (Coreference Resolution in Question-Answering): An open-source dataset and dataset creation methodology using instruction-following models

Rob Grzywinski · Joshua DArcy · Robert Naidoff · Ashish Shukla · Alex Browne · Ren Gibbons · Brinnae Bent

Keywords: [ Natural Language processing (NLP) ] [ question answering ] [ Information Retrieval ] [ large language models (LLMs) ] [ instruction following models ] [ coreference analysis ] [ automated dataset creation ]


Instruction-following language models demand robust methodologies for information retrieval to augment instructions for question-answering applications. A primary challenge is the resolution of coreferences in the context of chunking strategies for long documents. The critical barrier to experimentation of handling coreferences is a lack of open source datasets, specifically in question-answering tasks that require coreference resolution. In this work we present our Coreference Resolution in Question-Answering (CRaQAn) dataset, an open-source dataset that caters to the nuanced information retrieval requirements of coreference resolution in question-answering tasks by providing over 250 question-answer pairs containing coreferences. To develop this dataset, we developed a novel approach for creating high-quality datasets using an instruction-following model (GPT-4) and a Recursive Criticism and Improvement Loop.

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