---
title: "transformer_1376 at PsyDefDetect — paper by Saha et al. (2026)"
description: "The paper presents a system for the PsyDefDetect shared task at BioNLP 2026. 0.5508 F1. 13 references, cited by 1 paper here."
authors: ["Pritha Saha", "Shuvodwip Saha", "Anik Mahmud Shanto"]
year: 2026
venue: "Proceedings of the BioNLP 2026 (Shared Tasks)"
source: "https://aclanthology.org/2026.bionlp-2.15/"
doi: "10.18653/v1/2026.bionlp-2.15"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/saha-2026-transformer-1376-psydefdetect-qlora-based/"
---
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# transformer\_1376 at PsyDefDetect: A QLoRA-Based Generative Framework for Context-Aware Psychological Defense Mechanism Detection

**Authors:** [Pritha Saha](https://md.research.elamur.ai/free/en/authors/pritha-saha/), [Shuvodwip Saha](https://md.research.elamur.ai/free/en/authors/shuvodwip-saha/) and [Anik Mahmud Shanto](https://md.research.elamur.ai/free/en/authors/anik-mahmud-shanto/) · **Year:** 2026 · **Venue:** [Proceedings of the BioNLP 2026 (Shared Tasks)](https://md.research.elamur.ai/free/en/venues/bionlp/) ([BioNLP 2026](https://md.research.elamur.ai/free/en/venues/bionlp/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.bionlp-2.15/) · [PDF](https://aclanthology.org/2026.bionlp-2.15.pdf) · [DOI 10.18653/v1/2026.bionlp-2.15](https://doi.org/10.18653/v1/2026.bionlp-2.15) · [OpenAlex](https://openalex.org/W7166811314)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/saha-2026-transformer-1376-psydefdetect-qlora-based/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper presents a system for the PsyDefDetect shared task at BioNLP 2026. The task requires labeling a target utterance in a multi-turn emotional support dialogue with one of nine classes, which are derived from the Defense Mechanism Rating Scales (DMRS). These include a "no defense" label and a label for cases that lack enough context. The authors frame labeling as conditional generation rather than standard classification. Gemma-2-2B-IT receives a chat-formatted prompt with instructions, the preceding dialogue and the target turn, and learns to emit a single label token, with loss computed only on that token. Training combines 4-bit QLoRA (via Unsloth) on cloud T4 GPUs, random oversampling of rare classes, and left truncation of long contexts. On the blind test set the system reaches a Macro-F1 of 0.2475, ranking 12th of 21 teams. It clearly outperforms an in-house DeBERTa-v3-base classifier trained on the same data but falls short of the organizers' baseline (0.3148). The work shows that low-budget generative fine-tuning is workable for clinically grounded dialogue analysis, though with clear performance limits.

## Key points
- Data: PSYDEFCONV, built from a subset of ESConv. It has 200 dialogues and 4,709 utterances, of which 2,336 are annotated with 9 DMRS-based classes. Evaluation uses Macro-F1 over classes 1–8 and excludes the majority "No Defense" class.
- Method: Gemma-2-2B-IT with 4-bit QLoRA, LoRA adapters on all attention and feed-forward projections, and prompt-token masking. Training uses AdamW with warmup plus cosine decay at an effective batch size of 16, and inference uses greedy decoding.
- Results on the test set:
  - Proposed system: accuracy 0.5508, F1 0.2475.
  - DeBERTa-v3-base: accuracy 0.5500, F1 0.1376.
  - Official baseline: accuracy 0.6483, F1 0.3148.
- Gemma's chat template has no system role, so the instruction is merged into the user turn.
- Stated limitations include the small model size, possible overfitting from oversampling, reliance on fixed prompt templates, and the need for post-processing of generated labels.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Chittagong University of Engineering & Technology](https://md.research.elamur.ai/free/en/universities/chittagong-university-of-engineering-technology/) — University, Chittagong, Bangladesh · 28 works · 29 citations
- [Southeast University](https://md.research.elamur.ai/free/en/universities/southeast-university/) — University, Dhaka, Bangladesh · 9 works · 160 citations

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## Cite

Cite as: 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. Proceedings of the BioNLP 2026 (Shared Tasks).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{saha2026transformer,
  title = {{transformer\_1376 at PsyDefDetect: A QLoRA-Based Generative Framework for Context-Aware Psychological Defense Mechanism Detection}},
  author = {Pritha Saha, Shuvodwip Saha and Anik Mahmud Shanto},
  booktitle = {Proceedings of the BioNLP 2026 (Shared Tasks)},
  year = {2026},
  doi = {10.18653/v1/2026.bionlp-2.15},
  url = {https://doi.org/10.18653/v1/2026.bionlp-2.15}
}
```

- [← previous Tralaleros at SemEval-2026 Task 9: Multilingual Polarization Detection with Transformer-based Models](https://md.research.elamur.ai/free/en/papers/dahl-2026-tralaleros-semeval-2026-task-9/)
- [next → transformer_1376 at SemEval-2026 Task 9: A Multi-Stage Pipeline with Calibrated Ensembles and Lexical Post-Processing for Online Polarization Detection in Bengali](https://md.research.elamur.ai/free/en/papers/saha-2026-transformer-1376-semeval-2026-task/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/18/)

## References

13 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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6. Hongbin Na, Zimu Wang, Zhaoming Chen, Yining Hua, Rena Gao, Kailai Yang, Ling Chen, Wei Wang, Shaoxiong Ji, John Torous, and Sophia Ananiadou. 2026a. [Overview of the psydefdetect shared task at bionlp 2026: Detecting levels of psychological defense mechanisms in supportive conversations](https://md.research.elamur.ai/free/en/papers/na-2026-overview-psydefdetect-shared-task-bionlp/). In Proceedi…
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*Updated 1 November 2026*