Large language models (LLMs) have shown remarkable capabilities in natural language processing; however, they still face difficulties when tasked with understanding lengthy contexts and executing effective question answering. These challenges often arise due to the complexity and ambiguity present in longer texts. To enhance the performance of LLMs in such scenarios, we introduce the Long Question Coreference Adaptation (LQCA) method. This innovative framework focuses on coreference resolution tailored to long contexts, allowing the model to identify and manage references effectively. The LQCA method encompasses four key steps: resolving coreferences within sub-documents, computing the distances between mentions, defining a representative mention for coreference, and answering questions through mention replacement. By processing information systematically, the framework provides easier-to-handle partitions for LLMs, promoting better understanding. Experimental evaluations on a range of LLMs and datasets have yielded positive results, with a notable improvements on OpenAI-o1-mini and GPT-4o models, highlighting the effectiveness of leveraging coreference resolution to bridge context gaps in question answering.

翻译：大型语言模型（LLM）在自然语言处理领域展现出卓越能力；然而，当面临理解长篇幅上下文并执行有效问答的任务时，它们仍存在困难。这些挑战通常源于长文本中存在的复杂性和歧义性。为提升LLM在此类场景下的性能，我们提出了长问题指代适应（LQCA）方法。这一创新框架专注于针对长上下文的指代消解，使模型能够有效识别和处理指代关系。LQCA方法包含四个关键步骤：在子文档内解析指代关系、计算提及之间的距离、为指代链定义代表性提及、以及通过提及替换进行问题回答。通过系统化处理信息，该框架为LLM提供了更易于处理的文本分区，从而促进更好的理解。在一系列LLM和数据集上的实验评估取得了积极成果，尤其在OpenAI-o1-mini和GPT-4o模型上观察到显著提升，这凸显了利用指代消解来弥合问答中上下文鸿沟的有效性。