Summary of Natural Language Processing Relies on Linguistics, by Juri Opitz and Shira Wein and Nathan Schneider
Natural Language Processing RELIES on Linguistics
by Juri Opitz, Shira Wein, Nathan Schneider
First submitted to arxiv on: 9 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper explores the implications of Large Language Models (LLMs) on the future of linguistic expertise in NLP. It argues that despite LLMs’ ability to generate fluent text, NLP still relies heavily on linguistics in several key areas: Resources, Evaluation, Low-resource settings, Interpretability, Explanation, and Study of language. The paper highlights six major facets where linguistics contributes to NLP, making the case for the enduring importance of studying machine systems vis-à-vis human language. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are getting really good at generating text in certain languages! But what does this mean for the future of linguistic expertise in NLP? This paper says that even though LLMs can write well, we still need linguistics to help us with things like finding good data, testing how well models work, and making sense of how they learn. It also talks about situations where it’s hard to find enough training data, figuring out what’s going on inside the model’s head, explaining why a model works or doesn’t, and studying language itself. |
Keywords
» Artificial intelligence » Nlp
