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.

通过使用指令跟踪模型（GPT-4）和递归批评和改进循环，我们开发了一个高质量数据集，该数据集包含超过 250 个包含共指的问题 - 回答对，以满足共指解析在问答任务中的信息检索需求。

CRaQAn（问答中的指代消解）的发布：一个开源的数据集和数据集创建方法，使用指令遵循模型

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