"On the goals of linguistic theory": Revisiting Chomskyan theories in the era of AI
- URL: http://arxiv.org/abs/2411.10533v1
- Date: Fri, 15 Nov 2024 19:09:22 GMT
- Title: "On the goals of linguistic theory": Revisiting Chomskyan theories in the era of AI
- Authors: Eva Portelance, Masoud Jasbi,
- Abstract summary: Theoretical linguistics seeks to explain what human language is, and why.
Artificial intelligence models such as large language models are proving to have impressive linguistic capabilities.
Many are questioning what role, if any, such models should play in helping theoretical linguistics reach its ultimate research goals.
- Score: 0.20923359361008084
- License:
- Abstract: Theoretical linguistics seeks to explain what human language is, and why. Linguists and cognitive scientists have proposed different theoretical models of what language is, as well as cognitive factors that shape it, and allow humans to 'produce', 'understand', and 'acquire' natural languages. However, humans may no longer be the only ones learning to 'generate', 'parse', and 'learn' natural language: artificial intelligence (AI) models such as large language models are proving to have impressive linguistic capabilities. Many are thus questioning what role, if any, such models should play in helping theoretical linguistics reach its ultimate research goals? In this paper, we propose to answer this question, by reiterating the tenets of generative linguistics, a leading school of thought in the field, and by considering how AI models as theories of language relate to each of these important concepts. Specifically, we consider three foundational principles, finding roots in the early works of Noam Chomsky: (1) levels of theoretical adequacy; (2) procedures for linguistic theory development; (3) language learnability and Universal Grammar. In our discussions of each principle, we give special attention to two types of AI models: neural language models and neural grammar induction models. We will argue that such models, in particular neural grammar induction models, do have a role to play, but that this role is largely modulated by the stance one takes regarding each of these three guiding principles.
Related papers
- Proceedings of the First International Workshop on Next-Generation Language Models for Knowledge Representation and Reasoning (NeLaMKRR 2024) [16.282850445579857]
Reasoning is an essential component of human intelligence as it plays a fundamental role in our ability to think critically.
Recent leap forward in natural language processing, with the emergence of language models based on transformers, is hinting at the possibility that these models exhibit reasoning abilities.
Despite ongoing discussions about what reasoning is in language models, it is still not easy to pin down to what extent these models are actually capable of reasoning.
arXiv Detail & Related papers (2024-10-07T02:31:47Z) - A Philosophical Introduction to Language Models -- Part I: Continuity
With Classic Debates [0.05657375260432172]
This article serves both as a primer on language models for philosophers, and as an opinionated survey of their significance.
We argue that the success of language models challenges several long-held assumptions about artificial neural networks.
This sets the stage for the companion paper (Part II), which turns to novel empirical methods for probing the inner workings of language models.
arXiv Detail & Related papers (2024-01-08T14:12:31Z) - Formal Aspects of Language Modeling [74.16212987886013]
Large language models have become one of the most commonly deployed NLP inventions.
These notes are the accompaniment to the theoretical portion of the ETH Z"urich course on large language models.
arXiv Detail & Related papers (2023-11-07T20:21:42Z) - The Neuro-Symbolic Inverse Planning Engine (NIPE): Modeling
Probabilistic Social Inferences from Linguistic Inputs [50.32802502923367]
We study the process of language driving and influencing social reasoning in a probabilistic goal inference domain.
We propose a neuro-symbolic model that carries out goal inference from linguistic inputs of agent scenarios.
Our model closely matches human response patterns and better predicts human judgements than using an LLM alone.
arXiv Detail & Related papers (2023-06-25T19:38:01Z) - From Word Models to World Models: Translating from Natural Language to
the Probabilistic Language of Thought [124.40905824051079]
We propose rational meaning construction, a computational framework for language-informed thinking.
We frame linguistic meaning as a context-sensitive mapping from natural language into a probabilistic language of thought.
We show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings.
We extend our framework to integrate cognitively-motivated symbolic modules.
arXiv Detail & Related papers (2023-06-22T05:14:00Z) - Why can neural language models solve next-word prediction? A
mathematical perspective [53.807657273043446]
We study a class of formal languages that can be used to model real-world examples of English sentences.
Our proof highlights the different roles of the embedding layer and the fully connected component within the neural language model.
arXiv Detail & Related papers (2023-06-20T10:41:23Z) - Language Models as Inductive Reasoners [125.99461874008703]
We propose a new paradigm (task) for inductive reasoning, which is to induce natural language rules from natural language facts.
We create a dataset termed DEER containing 1.2k rule-fact pairs for the task, where rules and facts are written in natural language.
We provide the first and comprehensive analysis of how well pretrained language models can induce natural language rules from natural language facts.
arXiv Detail & Related papers (2022-12-21T11:12:14Z) - Language Models are not Models of Language [0.0]
Transfer learning has enabled large deep learning neural networks trained on the language modeling task to vastly improve performance.
We argue that the term language model is misleading because deep learning models are not theoretical models of language.
arXiv Detail & Related papers (2021-12-13T22:39:46Z) - Language Modelling as a Multi-Task Problem [12.48699285085636]
We investigate whether language models adhere to learning principles of multi-task learning during training.
Experiments demonstrate that a multi-task setting naturally emerges within the objective of the more general task of language modelling.
arXiv Detail & Related papers (2021-01-27T09:47:42Z) - Machine Common Sense [77.34726150561087]
Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI)
This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions.
arXiv Detail & Related papers (2020-06-15T13:59:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.