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        <title>transformer_1376 at PsyDefDetect: A QLoRA-Based Generative Framework for Context-Aware Psychological Defense Mechanism Detection</title>
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        <namePart type="given">Pritha</namePart>
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            <title>Proceedings of the BioNLP 2026 (Shared Tasks)</title>
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    <abstract>Psychological defense mechanisms play a cru-cial role in shaping human responses duringemotionally charged conversations, yet remainunderexplored in natural language processing.In this work, we address the PSYDEFCONVshared task, which involves classifying defensemechanisms in multi-turn dialogues using clin-ically grounded annotations based on the De-fense Mechanism Rating Scales (DMRS). Wepropose a generative supervised fine-tuningframework that reformulates the task as con-ditional text generation. A pre-trained causallanguage model (Gemma-2-2B) is adapted us-ing parameter-efficient fine-tuning (PEFT) with4-bit quantization, enabling efficient trainingunder limited computational resources. To han-dle class imbalance, we apply random oversam-pling, and we design a prompt-based input rep-resentation to incorporate conversational con-text effectively. Experimental results demon-strate that our generative approach is compet-itive with discriminative baselines while of-fering improved flexibility in modeling sub-tle and context-dependent defensive behaviors.The findings highlight the potential of genera-tive large language models for psychologicallygrounded dialogue understanding tasks.</abstract>
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%0 Conference Proceedings
%T transformer_1376 at PsyDefDetect: A QLoRA-Based Generative Framework for Context-Aware Psychological Defense Mechanism Detection
%A Saha, Pritha
%A Saha, Shuvodwip
%A Shanto, Anik Mahmud
%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 saha-etal-2026-transformer
%X Psychological defense mechanisms play a cru-cial role in shaping human responses duringemotionally charged conversations, yet remainunderexplored in natural language processing.In this work, we address the PSYDEFCONVshared task, which involves classifying defensemechanisms in multi-turn dialogues using clin-ically grounded annotations based on the De-fense Mechanism Rating Scales (DMRS). Wepropose a generative supervised fine-tuningframework that reformulates the task as con-ditional text generation. A pre-trained causallanguage model (Gemma-2-2B) is adapted us-ing parameter-efficient fine-tuning (PEFT) with4-bit quantization, enabling efficient trainingunder limited computational resources. To han-dle class imbalance, we apply random oversam-pling, and we design a prompt-based input rep-resentation to incorporate conversational con-text effectively. Experimental results demon-strate that our generative approach is compet-itive with discriminative baselines while of-fering improved flexibility in modeling sub-tle and context-dependent defensive behaviors.The findings highlight the potential of genera-tive large language models for psychologicallygrounded dialogue understanding tasks.
%U https://aclanthology.org/2026.bionlp-2.15/
%P 99-103

Markdown (Informal)

[transformer_1376 at PsyDefDetect: A QLoRA-Based Generative Framework for Context-Aware Psychological Defense Mechanism Detection](https://aclanthology.org/2026.bionlp-2.15/) (Saha et al., BioNLP 2026)

• transformer_1376 at PsyDefDetect: A QLoRA-Based Generative Framework for Context-Aware Psychological Defense Mechanism Detection (Saha et al., BioNLP 2026)

ACL

• Pritha Saha, Shuvodwip Saha, and Anik Mahmud Shanto. 2026. transformer_1376 at PsyDefDetect: A QLoRA-Based Generative Framework for Context-Aware Psychological Defense Mechanism Detection. In Proceedings of the BioNLP 2026 (Shared Tasks), pages 99–103, San Diego, California, USA. Association for Computational Linguistics.