Learning from Impairment: Leveraging Insights from Clinical Linguistics in Language Modelling Research
- URL: http://arxiv.org/abs/2412.15785v1
- Date: Fri, 20 Dec 2024 10:53:21 GMT
- Title: Learning from Impairment: Leveraging Insights from Clinical Linguistics in Language Modelling Research
- Authors: Dominique Brunato,
- Abstract summary: This position paper investigates the potential of integrating insights from language impairment research and its clinical treatment to develop human-inspired learning strategies and evaluation frameworks for language models (LMs)<n>We inspect the theoretical underpinnings underlying some influential linguistically motivated training approaches derived from neurolinguistics and, particularly, aphasiology, aimed at enhancing the recovery and generalization of linguistic skills in aphasia treatment.<n>We highlight how these insights can inform the design of rigorous assessments for LMs, specifically in their handling of complex syntactic phenomena, as well as their implications for developing human-like learning strategies.
- Score: 1.544681800932596
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This position paper investigates the potential of integrating insights from language impairment research and its clinical treatment to develop human-inspired learning strategies and evaluation frameworks for language models (LMs). We inspect the theoretical underpinnings underlying some influential linguistically motivated training approaches derived from neurolinguistics and, particularly, aphasiology, aimed at enhancing the recovery and generalization of linguistic skills in aphasia treatment, with a primary focus on those targeting the syntactic domain. We highlight how these insights can inform the design of rigorous assessments for LMs, specifically in their handling of complex syntactic phenomena, as well as their implications for developing human-like learning strategies, aligning with efforts to create more sustainable and cognitively plausible natural language processing (NLP) models.
Related papers
- A Computational Framework to Identify Self-Aspects in Text [9.187473897664105]
The Self is a multifaceted construct and it is reflected in language.<n>Many of the aspects of the Self align with psychological and other well-researched phenomena.<n>This proposal introduces a plan to develop a computational framework to identify Self-aspects in text.
arXiv Detail & Related papers (2025-07-17T13:31:04Z) - The Emergence of Abstract Thought in Large Language Models Beyond Any Language [95.50197866832772]
Large language models (LLMs) function effectively across a diverse range of languages.<n>Preliminary studies observe that the hidden activations of LLMs often resemble English, even when responding to non-English prompts.<n>Recent results show strong multilingual performance, even surpassing English performance on specific tasks in other languages.
arXiv Detail & Related papers (2025-06-11T16:00:54Z) - When Less Language is More: Language-Reasoning Disentanglement Makes LLMs Better Multilingual Reasoners [111.50503126693444]
We show that language-specific ablation consistently boosts multilingual reasoning performance.<n>Compared to post-training, our training-free ablation achieves comparable or superior results with minimal computational overhead.
arXiv Detail & Related papers (2025-05-21T08:35:05Z) - Applications of Artificial Intelligence for Cross-language Intelligibility Assessment of Dysarthric Speech [13.475654818182988]
This commentary introduces a conceptual framework to advance cross-language intelligibility assessment of dysarthric speech.<n>We propose a universal speech model that encodes dysarthric speech into acoustic-phonetic representations, followed by a language-specific intelligibility assessment model.
arXiv Detail & Related papers (2025-01-27T08:35:19Z) - Detecting Neurocognitive Disorders through Analyses of Topic Evolution and Cross-modal Consistency in Visual-Stimulated Narratives [84.03001845263]
Early detection of neurocognitive disorders (NCDs) is crucial for timely intervention and disease management.
Traditional narrative analysis often focuses on local indicators in microstructure, such as word usage and syntax.
We propose to investigate specific cognitive and linguistic challenges by analyzing topical shifts, temporal dynamics, and the coherence of narratives over time.
arXiv Detail & Related papers (2025-01-07T12:16:26Z) - 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) - A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection [35.31259047578382]
This review paper explores recent advances in deep learning approaches for non-invasive cognitive impairment detection.
We examine various non-invasive indicators of cognitive decline, including speech and language, facial, and motoric mobility.
Despite significant progress, several challenges remain, including data standardization and accessibility, model explainability, longitudinal analysis limitations, and clinical adaptation.
arXiv Detail & Related papers (2024-10-25T17:44:59Z) - The Role of Language Models in Modern Healthcare: A Comprehensive Review [2.048226951354646]
The application of large language models (LLMs) in healthcare has gained significant attention.
This review examines the trajectory of language models from their early stages to the current state-of-the-art LLMs.
arXiv Detail & Related papers (2024-09-25T12:15:15Z) - Diagnostic Reasoning in Natural Language: Computational Model and Application [68.47402386668846]
We investigate diagnostic abductive reasoning (DAR) in the context of language-grounded tasks (NL-DAR)
We propose a novel modeling framework for NL-DAR based on Pearl's structural causal models.
We use the resulting dataset to investigate the human decision-making process in NL-DAR.
arXiv Detail & Related papers (2024-09-09T06:55:37Z) - A Survey on Lexical Ambiguity Detection and Word Sense Disambiguation [0.0]
This paper explores techniques that focus on understanding and resolving ambiguity in language within the field of natural language processing (NLP)
It outlines diverse approaches ranging from deep learning techniques to leveraging lexical resources and knowledge graphs like WordNet.
The research identifies persistent challenges in the field, such as the scarcity of sense annotated corpora and the complexity of informal clinical texts.
arXiv Detail & Related papers (2024-03-24T12:58:48Z) - 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) - History, Development, and Principles of Large Language Models-An Introductory Survey [15.875687167037206]
Language models serve as a cornerstone in natural language processing (NLP)
Over extensive research spanning decades, language modeling has progressed from initial statistical language models (SLMs) to the contemporary landscape of large language models (LLMs)
arXiv Detail & Related papers (2024-02-10T01:18:15Z) - Evaluating the Efficacy of Interactive Language Therapy Based on LLM for
High-Functioning Autistic Adolescent Psychological Counseling [1.1780706927049207]
This study investigates the efficacy of Large Language Models (LLMs) in interactive language therapy for high-functioning autistic adolescents.
LLMs present a novel opportunity to augment traditional psychological counseling methods.
arXiv Detail & Related papers (2023-11-12T07:55:39Z) - Evaluating Large Language Models for Radiology Natural Language
Processing [68.98847776913381]
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP)
This study seeks to bridge this gap by critically evaluating thirty two LLMs in interpreting radiology reports.
arXiv Detail & Related papers (2023-07-25T17:57:18Z) - Assessing Language Disorders using Artificial Intelligence: a Paradigm
Shift [0.13393465195776774]
Speech, language, and communication deficits are present in most neurodegenerative syndromes.
We argue that using machine learning methodologies, natural language processing, and modern artificial intelligence (AI) for Language Assessment is an improvement over conventional manual assessment.
arXiv Detail & Related papers (2023-05-31T17:20:45Z) - Interactive Natural Language Processing [67.87925315773924]
Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP.
This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
arXiv Detail & Related papers (2023-05-22T17:18:29Z)
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.