NLP101: How can recent advances in large language models (LLMs) reshape traditional approaches to syntactic parsing and what implications does this have for linguistic theory and NLP applications?

How can recent advances in large language models (LLMs) reshape traditional approaches to syntactic parsing and what implications does this have for linguistic theory and NLP applications?

Trần Thu HươngHSU07 Hà - 2025年07月30日星期三 15:12

How can recent advances in large language models (LLMs) reshape traditional approaches to syntactic parsing and what implications does this have for linguistic theory and ...

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How can recent advances in large language models (LLMs) reshape traditional approaches to syntactic parsing and what implications does this have for linguistic theory and NLP applications?

Re: How can recent advances in large language models (LLMs) reshape traditional approaches to syntactic parsing and what implications does this have for linguistic theory and NLP applications?

AnhHSU07 Đào Tuyết - 2025年07月30日星期三 15:27

Recent breakthroughs in large language models (LLMs) such as GPT-4 are transformed syntactic parsing based on instruction of grammar models by-data and to exclude ...

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Recent breakthroughs in large language models (LLMs) such as GPT-4 are transformed syntactic parsing based on instruction of grammar models by-data and to exclude pre-existing grammatical rules. This gives parsing more flexibility and more precise in various scenarios and languages. In the case of NLP, it enhances things such as translation and summarization. To the field of linguistics, it has brought into dispute traditional theories based on rules and demonstrating the ability of models to comprehend syntax without specifically defined rules of grammar triggering recent discussions about the organization and processing of language in general.