Natural Language Processing RELIES on Linguistics

TL;DR

This paper discusses how NLP, especially with large language models, still fundamentally depends on linguistic principles across various aspects like resources, evaluation, and interpretability, emphasizing the ongoing importance of linguistics in AI development.

Contribution

It highlights six key areas where linguistics continues to influence NLP, advocating for the integration of linguistic insights in future AI research and applications.

Findings

NLP relies on linguistic resources and evaluation methods.

Linguistics informs interpretability and explanation in NLP.

The study emphasizes the enduring importance of linguistic knowledge in AI.

Abstract

Large Language Models (LLMs) have become capable of generating highly fluent text in certain languages, without modules specially designed to capture grammar or semantic coherence. What does this mean for the future of linguistic expertise in NLP? We highlight several aspects in which NLP (still) relies on linguistics, or where linguistic thinking can illuminate new directions. We argue our case around the acronym RELIES that encapsulates six major facets where linguistics contributes to NLP: Resources, Evaluation, Low-resource settings, Interpretability, Explanation, and the Study of language. This list is not exhaustive, nor is linguistics the main point of reference for every effort under these themes; but at a macro level, these facets highlight the enduring importance of studying machine systems vis-\`a-vis systems of human language.