---
title: "Natural Language Processing RELIES on Linguistics — Opitz 2025"
description: "Large language models now write fluent text without any built-in machinery for grammar or… 65 references, cited by 11 papers here."
authors: ["Juri Opitz", "Shira Wein", "Nathan Schneider"]
year: 2025
venue: "Computational Linguistics"
source: "https://aclanthology.org/2025.cl-3.9/"
doi: "10.1162/coli_a_00560"
licence: "CC BY-NC-ND 4.0 (Computational Linguistics, MIT Press)"
licence_url: "https://creativecommons.org/licenses/by-nc-nd/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/opitz-2025-natural-language-processing-relies-linguistics/"
---
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# Natural Language Processing RELIES on Linguistics

**Authors:** [Juri Opitz](https://md.research.elamur.ai/free/en/authors/juri-opitz/), [Shira Wein](https://md.research.elamur.ai/free/en/authors/shira-wein/) and [Nathan Schneider](https://md.research.elamur.ai/free/en/authors/nathan-schneider/) · **Year:** 2025 · **Venue:** [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/) ([Computational Linguistics 2025](https://md.research.elamur.ai/free/en/venues/cl/2025/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY-NC-ND 4.0 (Computational Linguistics, MIT Press)](https://creativecommons.org/licenses/by-nc-nd/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.cl-3.9/) · [PDF](https://aclanthology.org/2025.cl-3.9.pdf) · [DOI 10.1162/coli\_a\_00560](https://doi.org/10.1162/coli_a_00560) · [OpenAlex](https://openalex.org/W4409261669)

**Citations:** 2 citations (OpenAlex, 2026-10-14) · [11](https://md.research.elamur.ai/free/en/papers/opitz-2025-natural-language-processing-relies-linguistics/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary

Large language models now write fluent text without any built-in machinery for grammar or compositional meaning. The authors ask whether linguistic expertise still matters for NLP. This is a position piece from the journal's "Last Words" column, not an experimental study, and it introduces no new dataset, model, or benchmark. Instead, the authors review prior work and group it under the mnemonic RELIES: Resources, Evaluation, Low-resource settings, Interpretability, Explanation, and Study of language. For each area they show how knowledge about language shapes corpus selection and annotation, the design of human and automatic evaluation, work on under-resourced languages, the probing of black-box models, and tools for linguists, teachers, and language communities. They conclude that linguistics is not required for high accuracy on every task but still has clear engineering value. This is especially relevant for projects outside English, where typological diversity and scarce data make linguistic insight most useful.

## Key points

- **RELIES framework:** six facets where NLP depends on linguistics. The authors present the list as illustrative, not exhaustive, and do not claim linguistics is the only relevant expertise.
- **Two senses of CL:** a broad sense (computation plus natural language) and a narrow "cL", whose aim is answering questions about language itself rather than about technology.
- **Linguistically informed data selection:** Ravichander et al. (2022) chose data containing enough prepositions and complementizers. Ponti et al. (2020) used a typology index from Littell et al. (2017). The BabyLM challenge selects its data on cognitive-linguistic grounds.
- **No explicit linguistic biases in current LLMs:** the authors found no linguistic inductive biases in the published Llama 3.1 training procedure.
- **Active interdisciplinary venues and open challenges:** related workshops include LaTeCH-CLfL (8th edition, 2024), BEA (19th), Argument Mining (11th), and NLLP (3rd). The authors highlight three future challenges: preserving endangered languages, language-learning applications, and using symbolic representations for interpretability.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Zurich](https://md.research.elamur.ai/free/en/universities/university-of-zurich/) — University, Zurich, Switzerland · 78 works · 12,260 citations
- [Georgetown University](https://md.research.elamur.ai/free/en/universities/georgetown-university/) — University, Washington, United States · 88 works · 4,732 citations
- [Amherst College](https://md.research.elamur.ai/free/en/universities/amherst-college/) — University, Amherst Center, United States · 4 works · 13 citations

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## Cite

Cite as: Juri Opitz, Shira Wein and Nathan Schneider. 2025. Natural Language Processing RELIES on Linguistics. Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{opitz2025natural,
  title = {{Natural Language Processing RELIES on Linguistics}},
  author = {Juri Opitz, Shira Wein and Nathan Schneider},
  journal = {Computational Linguistics},
  year = {2025},
  doi = {10.1162/coli\_a\_00560},
  url = {https://doi.org/10.1162/coli_a_00560}
}
```

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## References

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*Updated 30 October 2026*