Looking forward: Linguistic theory and methods

• URL: http://arxiv.org/abs/2502.18313v1
• Date: Tue, 25 Feb 2025 16:03:15 GMT
• Title: Looking forward: Linguistic theory and methods
• Authors: John Mansfield, Ethan Gotlieb Wilcox,
• Abstract summary: Major themes shaping contemporary linguistics are explicit testing of hypotheses about symbolic representation, the impact of artificial neural networks, and the importance of intersubjectivity in linguistic theory.<n>By connecting linguistics with computer science, psychology, neuroscience, and biology, we provide a forward-looking perspective on the changing landscape of linguistic research.
• Score: 0.7673339435080445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This chapter examines current developments in linguistic theory and methods, focusing on the increasing integration of computational, cognitive, and evolutionary perspectives. We highlight four major themes shaping contemporary linguistics: (1) the explicit testing of hypotheses about symbolic representation, such as efficiency, locality, and conceptual semantic grounding; (2) the impact of artificial neural networks on theoretical debates and linguistic analysis; (3) the importance of intersubjectivity in linguistic theory; and (4) the growth of evolutionary linguistics. By connecting linguistics with computer science, psychology, neuroscience, and biology, we provide a forward-looking perspective on the changing landscape of linguistic research.

Related papers

• A Taxonomy of Linguistic Expressions That Contribute To Anthropomorphism of Language Technologies [55.99010491370177]
  anthropomorphism is the attribution of human-like qualities to non-human objects or entities.<n>To productively discuss the impacts of anthropomorphism, we need a shared vocabulary for the vast variety of ways that language can bemorphic.
  arXiv  Detail & Related papers  (2025-02-14T02:43:46Z)
• Human-like conceptual representations emerge from language prediction [72.5875173689788]
  Large language models (LLMs) trained exclusively through next-token prediction over language data exhibit remarkably human-like behaviors. Are these models developing concepts akin to humans, and if so, how are such concepts represented and organized? Our results demonstrate that LLMs can flexibly derive concepts from linguistic descriptions in relation to contextual cues about other concepts. These findings establish that structured, human-like conceptual representations can naturally emerge from language prediction without real-world grounding.
  arXiv  Detail & Related papers  (2025-01-21T23:54:17Z)
• Large Language Models as Neurolinguistic Subjects: Discrepancy in Performance and Competence for Form and Meaning [49.60849499134362]
  This study investigates the linguistic understanding of Large Language Models (LLMs) regarding signifier (form) and signified (meaning) We introduce a neurolinguistic approach, utilizing a novel method that combines minimal pair and diagnostic probing to analyze activation patterns across model layers. We found: (1) Psycholinguistic and neurolinguistic methods reveal that language performance and competence are distinct; (2) Direct probability measurement may not accurately assess linguistic competence; and (3) Instruction tuning won't change much competence but improve performance.
  arXiv  Detail & Related papers  (2024-11-12T04:16:44Z)
• Toward Cultural Interpretability: A Linguistic Anthropological Framework for Describing and Evaluating Large Language Models (LLMs) [13.71024600466761]
  This article proposes a new integration of linguistic anthropology and machine learning (ML) We show the theoretical feasibility of a new, conjoint field of inquiry, cultural interpretability (CI) CI emphasizes how the dynamic relationship between language and culture makes contextually sensitive, open-ended conversation possible.
  arXiv  Detail & Related papers  (2024-11-07T22:01:50Z)
• Constructive Approach to Bidirectional Causation between Qualia Structure and Language Emergence [5.906966694759679]
  This paper presents a novel perspective on the bidirectional causation between language emergence and relational structure of subjective experiences. We hypothesize that languages with distributional semantics, e.g., syntactic-semantic structures, may have emerged through the process of aligning internal representations among individuals.
  arXiv  Detail & Related papers  (2024-09-14T11:03:12Z)
• Language Evolution with Deep Learning [49.879239655532324]
  Computational modeling plays an essential role in the study of language emergence. It aims to simulate the conditions and learning processes that could trigger the emergence of a structured language. This chapter explores another class of computational models that have recently revolutionized the field of machine learning: deep learning models.
  arXiv  Detail & Related papers  (2024-03-18T16:52:54Z)
• Large language models as linguistic simulators and cognitive models in human research [0.0]
  The rise of large language models (LLMs) that generate human-like text has sparked debates over their potential to replace human participants in behavioral and cognitive research. We critically evaluate this replacement perspective to appraise the fundamental utility of language models in psychology and social science. This perspective reframes the role of language models in behavioral and cognitive science, serving as linguistic simulators and cognitive models that shed light on the similarities and differences between machine intelligence and human cognition and thoughts.
  arXiv  Detail & Related papers  (2024-02-06T23:28:23Z)
• Language Cognition and Language Computation -- Human and Machine Language Understanding [51.56546543716759]
  Language understanding is a key scientific issue in the fields of cognitive and computer science. Can a combination of the disciplines offer new insights for building intelligent language models?
  arXiv  Detail & Related papers  (2023-01-12T02:37:00Z)
• An Inclusive Notion of Text [69.36678873492373]
  We argue that clarity on the notion of text is crucial for reproducible and generalizable NLP. We introduce a two-tier taxonomy of linguistic and non-linguistic elements that are available in textual sources and can be used in NLP modeling.
  arXiv  Detail & Related papers  (2022-11-10T14:26:43Z)
• Perception Point: Identifying Critical Learning Periods in Speech for Bilingual Networks [58.24134321728942]
  We compare and identify cognitive aspects on deep neural-based visual lip-reading models. We observe a strong correlation between these theories in cognitive psychology and our unique modeling.
  arXiv  Detail & Related papers  (2021-10-13T05:30:50Z)
• Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks [30.580822082075475]
  We introduce three linguistic features that capture cross-cultural similarities that manifest in linguistic patterns and quantify distinct aspects of language pragmatics. Our analyses show that the proposed pragmatic features do capture cross-cultural similarities and align well with existing work in sociolinguistics and linguistic anthropology.
  arXiv  Detail & Related papers  (2020-06-16T17:20:25Z)
• Data-driven models and computational tools for neurolinguistics: a language technology perspective [12.082438928980087]
  We present a review of brain imaging-based neurolinguistic studies with a focus on the natural language representations. Mutual enrichment of neurolinguistics and language technologies leads to development of brain-aware natural language representations.
  arXiv  Detail & Related papers  (2020-03-23T20:41:51Z)
• Where New Words Are Born: Distributional Semantic Analysis of Neologisms and Their Semantic Neighborhoods [51.34667808471513]
  We investigate the importance of two factors, semantic sparsity and frequency growth rates of semantic neighbors, formalized in the distributional semantics paradigm. We show that both factors are predictive word emergence although we find more support for the latter hypothesis.
  arXiv  Detail & Related papers  (2020-01-21T19:09:49Z)

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.