arXiv Machine Learning August 4, 2026 By Anton Kolonin, Anna Arinicheva

Interpretable Recognition of Cognitive Distortions in Natural Language Texts

Read the original on arXiv Machine Learning →

arXiv:2511. 05969v2 Announce Type: replace-cross Abstract: We propose a new approach to multi-factor classification of natural language texts based on weighted structured patterns such as N-grams, taking into account the heterarchical relationships between them, applied to solve such a socially impactful problem as the automation of detection of specific cognitive distortions in psychological care, relying on an interpretable, robust and transparent artificial intelligence model.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 29

Cognivia: A Cognitive Behavioral Therapy Copilot for Evidence-Based Mental Healthcare

arXiv:2607. 25681v1 Announce Type: new Abstract: Cognitive distortion amplifies negative emotions and contributes to mental health disorders.

By Qi Chen, Siria Xiyueyao Luo, Jian Wang, Yuan Shi, Haocong Rao, Xuejiao Zhao

llms fine-tuning

arXiv Machine Learning

Jun 19

Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings

arXiv:2104. 08928v4 Announce Type: replace-cross Abstract: Unstructured text provides decision-makers with a rich data source in many domains, ranging from product reviews in retail to nursing notes in healthcare.

By Kan Xu, Xuanyi Zhao, Hamsa Bastani, Osbert Bastani

rag nlp fine-tuning benchmarks

arXiv AI

Jun 2

Addressing Longstanding Challenges in Cognitive Science with Language Models

arXiv:2511. 00206v3 Announce Type: replace Abstract: Cognitive science faces ongoing challenges in research integration, formalization, conceptual clarity, and other areas, in part due to its multifaceted and interdisciplinary nature.

By Dirk U. Wulff, Rui Mata

llms safety

arXiv AI

Jul 9

Comprehensive Evaluation of Large Language Model Responses: A Multi-Factor Scoring System

arXiv:2607. 06940v1 Announce Type: cross Abstract: The remarkable performance of large language models (LLMs) in linguistic tasks underscores an urgent need for comprehensive evaluation of their response quality.

By Yiming Gai, Junde Lu, Xuefei Huang

llms

arXiv AI

Jul 14

Polarization Detection: A Hybrid Approach with AfroXLMR-Social and DeBERTa for Low- and High-Resource Settings

arXiv:2607. 10312v1 Announce Type: cross Abstract: The rapid proliferation of online polarization threatens social cohesion, necessitating robust automated detection systems that operate effectively across diverse linguistic contexts.

By Muhammad Abdullahi Said

fine-tuning

arXiv AI

Jul 15

Evaluating Health Misinformation in Low-Resource Languages: Integrating Small Language Models with a Culturally-Sensitive Responsible NLP Framework (Bangla as a Case Study)

arXiv:2607. 12336v1 Announce Type: cross Abstract: Artificial Intelligence (AI) technologies, while serving as a foundational enabler for modern social media and digital health services, exert a bivalent effect by simultaneously acting as a combatant against and a spread vector for misinformation.

By Farnaz Farid, Raihan Alam, Al Al-Areqi, Farhad Ahamed, Muhammad Hassan Khan, Sadia Hossain, Irena Veljanova, Anika Tabassum Binte Hossain

llms nlp fine-tuning

Related stories

Cognivia: A Cognitive Behavioral Therapy Copilot for Evidence-Based Mental Healthcare

Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings

Addressing Longstanding Challenges in Cognitive Science with Language Models

Comprehensive Evaluation of Large Language Model Responses: A Multi-Factor Scoring System

Polarization Detection: A Hybrid Approach with AfroXLMR-Social and DeBERTa for Low- and High-Resource Settings

Evaluating Health Misinformation in Low-Resource Languages: Integrating Small Language Models with a Culturally-Sensitive Responsible NLP Framework (Bangla as a Case Study)