Natural Language Processing RELIES on Linguistics

2024-05-09

arXiv.org

AI Summary

Recent large language models (LLMs) exhibit a superficial “de-linguistification” trend, marginalizing linguistics despite its foundational relevance to natural language processing (NLP). Method: This paper systematically reasserts linguistics’ indispensable structural role in NLP through an original six-dimensional RELIES framework—encompassing Resources, Evaluation, Low-resource settings, Interpretability, Explanation, and Study of language—and integrates linguistic insights via conceptual analysis, interdisciplinary synthesis, and empirical case studies. Contribution/Results: The work challenges the purely data-driven paradigm by demonstrating how linguistic theory methodologically anchors model architecture design, evaluation criteria, and ethical governance. It establishes linguistics not as auxiliary but as constitutive to NLP’s scientific rigor and human-centered grounding, offering a systematic, theory-informed roadmap for developing linguistically principled, interpretable, and equitable NLP systems.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Language and Vision

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Examines NLP's reliance on linguistics for grammar and semantics.
• Explores linguistic contributions to NLP in low-resource settings.
• Highlights linguistics' role in NLP interpretability and explanation.

Innovation

• LLMs generate fluent text without grammar modules
• Linguistics contributes to NLP via RELIES framework
• Study of human language informs machine systems