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Nürnberg NLP at PsyDefDetect: Multi-Axis Voter Ensembles for Psychological Defence Mechanism Classification

Philipp Steigerwald, Eric Rudolph, Jens Albrecht · May 8, 2026 · Citations: 0

Automatic Metrics General Red Team
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How to use this page

High trust

Use this as a practical starting point for protocol research, then validate against the original paper.

Best use

Primary benchmark and eval reference

What to verify

Validate the exact study setup in the full paper before operational use.

Evidence quality

High

Derived from extracted protocol signals and abstract evidence.

Abstract

Detecting levels of psychological defence mechanisms in supportive conversations is inherently ambiguous. In the PsyDefDetect shared task at BioNLP 2026 the eight positive defence categories share surface language and differ only in pragmatic function and trained raters reach only moderate inter-annotator agreement. On such a task the decisive lever is not a stronger single model but error independence, since any single representation will waver on the overlapping defence boundaries. We translate this insight into a 9-voter ensemble spanning three orthogonal axes: class granularity (all nine classes for the gatekeeper, only the eight defence classes for the specialists), training method (generative and discriminative) and base model. The system reaches $F1_{test}{=}.420$ on the hidden test set, placing first among 21 registered teams.

Should You Rely On This Paper?

This paper has strong direct human-feedback and evaluation protocol signal and is suitable as a primary eval pipeline reference.

Best use

Primary benchmark and eval reference

Use if you need

A concrete protocol example with enough signal to inform rater workflow design.

Main weakness

No major weakness surfaced.

Trust level

High

Usefulness score

75/100 • High

Use this as a primary source when designing or comparing eval protocols.

Human Feedback Signal

Detected

Evaluation Signal

Detected

Usefulness for eval research

High-confidence candidate

Extraction confidence 80%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

strong

Red Team

Directly usable for protocol triage.

"Detecting levels of psychological defence mechanisms in supportive conversations is inherently ambiguous."

Evaluation Modes

strong

Automatic Metrics

Includes extracted eval setup.

"Detecting levels of psychological defence mechanisms in supportive conversations is inherently ambiguous."

Quality Controls

strong

Inter Annotator Agreement Reported

Calibration/adjudication style controls detected.

"Detecting levels of psychological defence mechanisms in supportive conversations is inherently ambiguous."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Detecting levels of psychological defence mechanisms in supportive conversations is inherently ambiguous."

Reported Metrics

strong

F1, Agreement

Useful for evaluation criteria comparison.

"In the PsyDefDetect shared task at BioNLP 2026 the eight positive defence categories share surface language and differ only in pragmatic function and trained raters reach only moderate inter-annotator agreement."

Human Feedback Details

  • Uses human feedback: Yes
  • Feedback types: Red Team
  • Rater population: Not reported
  • Expertise required: General

Evaluation Details

  • Evaluation modes: Automatic Metrics
  • Agentic eval: None
  • Quality controls: Inter Annotator Agreement Reported
  • Evidence quality: High
  • Use this page as: Primary benchmark and eval reference

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

f1agreement

Research Brief

Metadata summary

Detecting levels of psychological defence mechanisms in supportive conversations is inherently ambiguous.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Detecting levels of psychological defence mechanisms in supportive conversations is inherently ambiguous.
  • In the PsyDefDetect shared task at BioNLP 2026 the eight positive defence categories share surface language and differ only in pragmatic function and trained raters reach only moderate inter-annotator agreement.
  • On such a task the decisive lever is not a stronger single model but error independence, since any single representation will waver on the overlapping defence boundaries.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Research Summary

Contribution Summary

  • In the PsyDefDetect shared task at BioNLP 2026 the eight positive defence categories share surface language and differ only in pragmatic function and trained raters reach only moderate inter-annotator agreement.
  • The system reaches F1_{test}{=}.420 on the hidden test set, placing first among 21 registered teams.

Why It Matters For Eval

  • In the PsyDefDetect shared task at BioNLP 2026 the eight positive defence categories share surface language and differ only in pragmatic function and trained raters reach only moderate inter-annotator agreement.

Researcher Checklist

  • Pass: Human feedback protocol is explicit

    Detected: Red Team

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Pass: Quality control reporting appears

    Detected: Inter Annotator Agreement Reported

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Pass: Metric reporting is present

    Detected: f1, agreement

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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