@inproceedings{babakova-etal-2026-explainators,
title = "Explainators at {P}sy{D}ef{D}etect: Hierarchical Prompting and Representation-Based Classification for Psychological Defenses",
author = "Babakova, Liudmila and
Luongo-Vazquez, Christopher and
Stepin, Ilia",
editor = "Gupta, Deepak and
Demner-Fushman, Dina",
booktitle = "Proceedings of the {B}io{NLP} 2026 (Shared Tasks)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.bionlp-2.16/",
pages = "104--108",
ISBN = "979-8-89176-435-4",
abstract = "Psychological defense detection is one of essential present-day challenges in clinical practice. The state-of-the-art natural language processing (NLP) tools aim to automate this task. However, their potential and efficiency remain largely unexplored. This manuscript attempts to address this problem from various perspectives: it first explores the efficiency of direct large language model (LLM)-prompting. Then, it applies NLP techniques for LLM fine-tuning applied to the psychological defense classification task. Finally, it attempts to generate states of mind based on the speaker{'}s psychological state. The results show that the complexity of the task requires further improvement of the software solutions used."
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<abstract>Psychological defense detection is one of essential present-day challenges in clinical practice. The state-of-the-art natural language processing (NLP) tools aim to automate this task. However, their potential and efficiency remain largely unexplored. This manuscript attempts to address this problem from various perspectives: it first explores the efficiency of direct large language model (LLM)-prompting. Then, it applies NLP techniques for LLM fine-tuning applied to the psychological defense classification task. Finally, it attempts to generate states of mind based on the speaker’s psychological state. The results show that the complexity of the task requires further improvement of the software solutions used.</abstract>
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%0 Conference Proceedings
%T Explainators at PsyDefDetect: Hierarchical Prompting and Representation-Based Classification for Psychological Defenses
%A Babakova, Liudmila
%A Luongo-Vazquez, Christopher
%A Stepin, Ilia
%Y Gupta, Deepak
%Y Demner-Fushman, Dina
%S Proceedings of the BioNLP 2026 (Shared Tasks)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-435-4
%F babakova-etal-2026-explainators
%X Psychological defense detection is one of essential present-day challenges in clinical practice. The state-of-the-art natural language processing (NLP) tools aim to automate this task. However, their potential and efficiency remain largely unexplored. This manuscript attempts to address this problem from various perspectives: it first explores the efficiency of direct large language model (LLM)-prompting. Then, it applies NLP techniques for LLM fine-tuning applied to the psychological defense classification task. Finally, it attempts to generate states of mind based on the speaker’s psychological state. The results show that the complexity of the task requires further improvement of the software solutions used.
%U https://aclanthology.org/2026.bionlp-2.16/
%P 104-108
Markdown (Informal)
[Explainators at PsyDefDetect: Hierarchical Prompting and Representation-Based Classification for Psychological Defenses](https://aclanthology.org/2026.bionlp-2.16/) (Babakova et al., BioNLP 2026)
ACL