Language understanding is a multi-faceted cognitive capability, which the Natural Language Processing (NLP) community has striven to model computationally for decades. Traditionally, facets of linguistic intelligence have been compartmentalized into tasks with specialized model architectures and corresponding evaluation protocols. With the advent of large language models (LLMs) the community has witnessed a dramatic shift towards general purpose, task-agnostic approaches powered by generative models. As a consequence, the traditional compartmentalized notion of language tasks is breaking down, followed by an increasing challenge for evaluation and analysis. At the same time, LLMs are being deployed in more real-world scenarios, including previously unforeseen zero-shot setups, increasing the need for trustworthy and reliable systems. Therefore, we argue that it is time to rethink what constitutes tasks and model evaluation in NLP, and pursue a more holistic view on language, placing trustworthiness at the center. Towards this goal, we review existing compartmentalized approaches for understanding the origins of a model's functional capacity, and provide recommendations for more multi-faceted evaluation protocols.

翻译：语言理解是一种多方面的认知能力，自然语言处理（NLP）领域数十年来一直致力于对其进行计算建模。传统上，语言智能的不同层面被划分为独立任务，并采用专门的模型架构及相应的评估协议。随着大型语言模型（LLMs）的出现，该领域经历了一场重大转变，转向由生成式模型驱动的通用、任务无关方法。这一趋势导致传统上对语言任务的分割式理解逐渐瓦解，随之而来的评估与分析挑战也日益增加。与此同时，LLMs正被部署至更多真实场景，包括此前未曾预见的零样本设定，从而对系统的可信赖性与可靠性提出了更高要求。因此，我们主张是时候重新审视NLP中任务与模型评估的构成，追求一种以可信赖性为核心、更全面的语言观。为实现这一目标，我们回顾了现有理解模型功能能力来源的分割式方法，并提出了更具多维性的评估协议建议。