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.Comment: Accepted at EMNLP 2023 (Main Conference), camera-read