Abstract
A broad range of NLP tasks involve selecting relevant text spans from given source texts. Despite this shared objective, such \textit{content selection} tasks have traditionally been studied in isolation, each with its own modeling approaches, datasets, and evaluation metrics. In this work, we propose \textit{instruction-guided content selection (IGCS)} as a beneficial unified framework for such settings, where the task definition and any instance-specific request are encapsulated as instructions to a language model. To promote this framework, we introduce \igcsbench{}, the first unified benchmark covering diverse content selection tasks. Further, we create a large generic synthetic dataset that can be leveraged for diverse content selection tasks, and show that transfer learning with these datasets often boosts performance, whether dedicated training for the targeted task is available or not. Finally, we address generic inference time issues that arise in LLM-based modeling of content selection, assess a generic evaluation metric, and overall propose the utility of our resources and methods for future content selection models. Models and datasets available at this https URL.
Abstract (translated)
许多自然语言处理（NLP）任务都涉及到从给定的源文本中选择相关的文本片段。尽管这些任务有着共同的目标，即“内容选择”，但它们通常被孤立地研究，每个任务都有自己特定的建模方法、数据集和评估指标。在本工作中，我们提出了**指令引导的内容选择（IGCS）**作为此类设置的一个有益的统一框架，在该框架中，任务定义以及任何实例特定的要求都被封装为提供给语言模型的指令。 为了推广这一框架，我们引入了**\igcsbench**，这是第一个涵盖多种内容选择任务的统一基准。此外，我们创建了一个大型通用合成数据集，它可以用于各种内容选择任务，并展示了使用这些数据集进行迁移学习通常可以提高性能，无论是否为特定任务提供了专门训练的数据。 最后，我们解决了基于大语言模型（LLM）的内容选择建模在推理时间上的一般性问题，评估了一般的评价指标，并总体上提出了我们的资源和方法对未来内容选择模型的实用性。相关模型和数据集可在[此链接](https://this https URL)获取。
URL
https://arxiv.org/abs/2507.16922
