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Useful blunders: Can automated speech recognition errors improve downstream dementia classification?, , , и . J. Biomed. Informatics, (2024)MAIT: Multi-agent Local Observation Interaction to Improve the Decision-Making Ability., , , , , и . ICIC (2), том 14863 из Lecture Notes in Computer Science, стр. 407-418. Springer, (2024)Condition Unknown: Predicting Patients' Health Conditions in an Online Health Community., , , и . CSCW Companion, стр. 281-284. ACM, (2018)GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate Degradation of Artificial Neural Language Models., , , , и . ACL (1), стр. 1866-1877. Association for Computational Linguistics, (2022)Too Big to Fail: Larger Language Models are Disproportionately Resilient to Induction of Dementia-Related Linguistic Anomalies., , , и . ACL (Findings), стр. 6363-6377. Association for Computational Linguistics, (2024)Crossing the "Cookie Theft" Corpus Chasm: Applying What BERT Learns From Outside Data to the ADReSS Challenge Dementia Detection Task., , , , и . Frontiers Comput. Sci., (2021)TRESTLE: Toolkit for Reproducible Execution of Speech, Text and Language Experiments., , , и . CoRR, (2023)The Far Side of Failure: Investigating the Impact of Speech Recognition Errors on Subsequent Dementia Classification., , и . CoRR, (2022)Reexamining Racial Disparities in Automatic Speech Recognition Performance: The Role of Confounding by Provenance., , и . CoRR, (2024)Language Models Resist Alignment., , , , , , , и . CoRR, (2024)