Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip
<p>This study explores the use of pre-trained language models (PLMs) in tracking priming treatment induced language recovery in aphasia. We evaluate PLM-derived surprisals, the negative log-probabilities of a word or a sequence of words calculated by a PLM given its preceding context, as a con...
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2025
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| _version_ | 1852015373762691072 |
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| author | Yan Cong (1491478) |
| author2 | Jiyeon Lee (77903) |
| author2_role | author |
| author_facet | Yan Cong (1491478) Jiyeon Lee (77903) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yan Cong (1491478) Jiyeon Lee (77903) |
| dc.date.none.fl_str_mv | 2025-10-30T06:21:47Z |
| dc.identifier.none.fl_str_mv | 10.3389/frai.2025.1668399.s001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Data_Sheet_1_Tracking_priming-induced_language_recovery_in_aphasia_with_pre-trained_language_models_zip/30486713 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Knowledge Representation and Machine Learning GenAI large language models language rehabilitation aphasia recovery structural priming as treatment prompt engineering aphasia automatic clinical assessment |
| dc.title.none.fl_str_mv | Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>This study explores the use of pre-trained language models (PLMs) in tracking priming treatment induced language recovery in aphasia. We evaluate PLM-derived surprisals, the negative log-probabilities of a word or a sequence of words calculated by a PLM given its preceding context, as a continuous and interpretable measure of treatment-induced language change. We found that surprisal scores decreased following structural priming treatment, especially in participants with more severe sentence production impairments. We also introduce a prompting-based pipeline for clinical classification tasks. It achieved promising results in classifying aphasia sentence correctness (F1 = 0.967) and detecting error categories in aphasia (accuracy = 0.846). Such use of PLMs for modeling, tracking, and automatically classifying language recovery in aphasia represents a promising deployment of GenAI in a clinical rehabilitation setting. Together, our PLM-based analyses offer a practical approach for modeling language rehabilitation, tracking not only language structure but also individual change over time in clinical contexts.</p>Clinical trial registration<p>Identifier NTC05415501.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_ea7c19a13485248f8a20344d01c33bdb |
| identifier_str_mv | 10.3389/frai.2025.1668399.s001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30486713 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zipYan Cong (1491478)Jiyeon Lee (77903)Knowledge Representation and Machine LearningGenAIlarge language modelslanguage rehabilitationaphasia recoverystructural priming as treatmentprompt engineeringaphasiaautomatic clinical assessment<p>This study explores the use of pre-trained language models (PLMs) in tracking priming treatment induced language recovery in aphasia. We evaluate PLM-derived surprisals, the negative log-probabilities of a word or a sequence of words calculated by a PLM given its preceding context, as a continuous and interpretable measure of treatment-induced language change. We found that surprisal scores decreased following structural priming treatment, especially in participants with more severe sentence production impairments. We also introduce a prompting-based pipeline for clinical classification tasks. It achieved promising results in classifying aphasia sentence correctness (F1 = 0.967) and detecting error categories in aphasia (accuracy = 0.846). Such use of PLMs for modeling, tracking, and automatically classifying language recovery in aphasia represents a promising deployment of GenAI in a clinical rehabilitation setting. Together, our PLM-based analyses offer a practical approach for modeling language rehabilitation, tracking not only language structure but also individual change over time in clinical contexts.</p>Clinical trial registration<p>Identifier NTC05415501.</p>2025-10-30T06:21:47ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.3389/frai.2025.1668399.s001https://figshare.com/articles/dataset/Data_Sheet_1_Tracking_priming-induced_language_recovery_in_aphasia_with_pre-trained_language_models_zip/30486713CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/304867132025-10-30T06:21:47Z |
| spellingShingle | Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip Yan Cong (1491478) Knowledge Representation and Machine Learning GenAI large language models language rehabilitation aphasia recovery structural priming as treatment prompt engineering aphasia automatic clinical assessment |
| status_str | publishedVersion |
| title | Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip |
| title_full | Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip |
| title_fullStr | Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip |
| title_full_unstemmed | Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip |
| title_short | Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip |
| title_sort | Data Sheet 1_Tracking priming-induced language recovery in aphasia with pre-trained language models.zip |
| topic | Knowledge Representation and Machine Learning GenAI large language models language rehabilitation aphasia recovery structural priming as treatment prompt engineering aphasia automatic clinical assessment |