mlsanityData to 14 Oct 2026

Signs of Struggle: Spotting Cognitive Distortions across Language and Register

arXiv 2508.20771 ↗First posted 28 Aug 2025cs.CL cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Rising mental health issues among youth have increased interest in automated approaches for detecting early signs of psychological distress in digital text. One key focus is the identification of cognitive distortions, irrational thought patterns that have a role in aggravating mental distress. Early detection of these distortions may enable timely, low-cost interventions. While prior work has focused on English clinical data, we present the first in-depth study of cross-lingual and cross-register generalization of cognitive distortion detection, analyzing forum posts written by Dutch adolescents. Our findings show that while changes in language and writing style can significantly affect model performance, domain adaptation methods show the most promise.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 59 references that are in the map. First posted on arXiv 28 Aug 2025.

Builds on

14 of 59 references.

References · 14

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  2. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
  3. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  4. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  5. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,173
  6. 2020Neural Unsupervised Domain Adaptation in NLP---A Surveycited by 144
  7. 2023Cognitive Reframing of Negative Thoughts through Human-Language Model Interactioncited by 77
  8. 2016Empath: Understanding Topic Signals in Large-Scale Textcited by 51
  9. 2023Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Promptingcited by 31
  10. 2024ERD: A Framework for Improving LLM Reasoning for Cognitive Distortion Classificationcited by 14
  11. 2024Large Language Models are Capable of Offering Cognitive Reappraisal, if Guidedcited by 13
  12. 2022Mere Contrastive Learning for Cross-Domain Sentiment Analysiscited by 8
2 more
  1. 2023DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptationcited by 2
  2. 2024Stochastic Adversarial Networks for Multi-Domain Text Classificationcited by 1

Built on by

Not cited yet.