Human Feedback Types
strongRed Team
Directly usable for protocol triage.
"Detecting levels of psychological defence mechanisms in supportive conversations is inherently ambiguous."
HFEPX · Eval paper review
Nürnberg NLP at PsyDefDetect: Multi-Axis Voter Ensembles for Psychological Defence Mechanism Classification
Philipp Steigerwald, Eric Rudolph, Jens Albrecht
Published
May 8, 2026
Citations
0
Trust level
High
Usefulness score
75/100 (High)
Extraction confidence
80% (High)
Derived from extracted protocol signals and abstract evidence.
Rater population
Not reported
Signals refreshed
May 8, 2026
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.
Use this as a practical starting point for protocol research, then validate against the original paper.
Best use
Primary benchmark and eval reference
Use if you need
A concrete protocol example with enough signal to inform rater workflow design.
What to verify
Validate the exact study setup in the full paper before operational use.
Main weakness
No major weakness surfaced.
Human feedback signal
Detected
From extracted signals
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
75/100
High-confidence candidate
Use this as a primary source when designing or comparing eval protocols.
If you are doing eval pipeline work, start here
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.
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.
Evaluation Modes
strongAutomatic Metrics
Includes extracted eval setup.
"Detecting levels of psychological defence mechanisms in supportive conversations is inherently ambiguous."
Quality Controls
strongInter Annotator Agreement Reported
Calibration/adjudication style controls detected.
"Detecting levels of psychological defence mechanisms in supportive conversations is inherently ambiguous."
Benchmarks / Datasets
missingNot extracted
No benchmark anchors detected.
"Detecting levels of psychological defence mechanisms in supportive conversations is inherently ambiguous."
Reported Metrics
strongF1, 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."
Benchmarks and datasets
No benchmark or dataset names were extracted from the available abstract.
Reported metrics
f1agreement
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
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.
Recommended queries
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
-
Human feedback protocol is explicit
Detected: Red Team
-
Evaluation mode is explicit
Detected: Automatic Metrics
-
Quality control reporting appears
Detected: Inter Annotator Agreement Reported
-
Benchmark or dataset anchors are present
No benchmark/dataset anchor extracted from abstract.
-
Metric reporting is present
Detected: f1, agreement