Juri Opitz, Shira Wein, Nathan Schneider
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-à-vis systems of human language.
| Comments: | To appear in Computational Linguistics. This is a pre-MIT Press publication version |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2405.05966 [cs.CL] |
| (or arXiv:2405.05966v4 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2405.05966Focus to learn more |
Submission history
From: Juri Opitz [view email]
[v1] Thu, 9 May 2024 17:59:32 UTC (145 KB)
[v2] Mon, 9 Sep 2024 08:21:13 UTC (172 KB)
[v3] Fri, 22 Nov 2024 15:36:32 UTC (195 KB)
[v4] Mon, 10 Mar 2025 15:07:49 UTC (187 KB)
This abstract compellingly addresses the relationship between large language models (LLMs) and linguistics in NLP, focusing on the continuing significance of linguistic expertise despite the advancements in fluent, grammar-agnostic text generation.
Strengths:
- Timely and Relevant Topic:
The discussion around how LLMs generate fluent language without explicit linguistic modules taps into a critical and current debate in NLP about the role of linguistic knowledge. - Clear Conceptual Framework:
Using the acronym RELIES to organize six major facets—Resources, Evaluation, Low-resource settings, Interpretability, Explanation, and Study of language—provides a memorable, structured way to present the areas where linguistics remains crucial in NLP. - Balanced and Nuanced Position:
The abstract acknowledges that linguistics is not the sole or main reference for every effort but underscores its macro-level importance, avoiding overstated claims. - Focus on Broader Impact:
It emphasizes how linguistic thinking can illuminate new research directions, signifying that studying human language systems is vital for advancing machine systems.
Areas for Improvement:
- Concrete Examples:
The abstract could be strengthened by briefly mentioning illustrative examples or concrete NLP tasks where linguistics makes a substantial impact (e.g., parsing, semantic role labeling, interpretability research). - Clarification of Novelty:
As this is more a conceptual or position piece, highlighting what new perspectives or syntheses the paper offers beyond existing literature would clarify its contribution. - Flow and Accessibility:
Slight refinement in sentence flow—for example, the phrase “What does this mean for the future of linguistic expertise in NLP?” is somewhat abrupt. A smoother introduction to this question would improve readability.
Overall Impression:
This abstract effectively frames an important ongoing dialogue about the integration of linguistic expertise in contemporary NLP amidst the rise of LLMs. The RELIES framework is a valuable conceptual tool for understanding this relationship. Adding concrete examples and clarifying the unique contribution of the paper would enhance its persuasive power and accessibility.
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