mlsanityData to 7 Oct 2026

Hacia la interpretabilidad de la detecci'on anticipada de riesgos de depresi'on utilizando grandes modelos de lenguaje

arXiv 2503.20939 ↗First posted 26 Mar 2025cs.CLcited by 1

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Abstract (arXiv)

Early Detection of Risks (EDR) on the Web involves identifying at-risk users as early as possible. Although Large Language Models (LLMs) have proven to solve various linguistic tasks efficiently, assessing their reasoning ability in specific domains is crucial. In this work, we propose a method for solving depression-related EDR using LLMs on Spanish texts, with responses that can be interpreted by humans. We define a reasoning criterion to analyze users through a specialist, apply in-context learning to the Gemini model, and evaluate its performance both quantitatively and qualitatively. The results show that accurate predictions can be obtained, supported by explanatory reasoning, providing a deeper understanding of the solution. Our approach offers new perspectives for addressing EDR problems by leveraging the power of LLMs.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 24 references that are in the map. First posted on arXiv 26 Mar 2025.

Builds on

14 of 24 references.

References · 14

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2023Let's Verify Step by Stepcited by 3,598
  3. 2022Emergent Abilities of Large Language Modelscited by 2,483
  4. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  5. 2017Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)cited by 1,309
  6. 2022STaR: Bootstrapping Reasoning With Reasoningcited by 895
  7. 2023Bias and Fairness in Large Language Models: A Surveycited by 626
  8. 2023Explainability for Large Language Models: A Surveycited by 331
  9. 2022Can language models learn from explanations in context?cited by 248
  10. 2024OpenMoE: An Early Effort on Open Mixture-of-Experts Language Modelscited by 185
  11. 2024Reasoning Abilities of Large Language Models: In-Depth Analysis on the Abstraction and Reasoning Corpuscited by 32
  12. 2023Challenges of Large Language Models for Mental Health Counselingcited by 16
2 more
  1. 2024Beyond Words: On Large Language Models Actionability in Mission-Critical Risk Analysiscited by 9
  2. 2023Early Detection of Depression and Eating Disorders in Spanish: UNSL at MentalRiskES 2023cited by 5

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Tackling a Challenging Corpus for Early Detection of Gambling Disorder: UNSL at MentalRiskES 2025cited by 0

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  1. 2025Systematic Evaluation of Machine-Generated Reasoning and PHQ-9 Labeling for Depression Detection Using Large Language Models
  2. 2023Towards Interpretable Mental Health Analysis with Large Language Models
  3. 2023Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
  4. 2025Cognitive-Mental-LLM: Evaluating Reasoning in Large Language Models for Mental Health Prediction via Online Text
  5. 2025Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models
  6. 2025Does Rationale Quality Matter? Enhancing Mental Disorder Detection via Selective Reasoning Distillation
  7. 2025Evaluating Reasoning LLMs for Suicide Screening with the Columbia-Suicide Severity Rating Scale
  8. 2025A Survey on Enhancing Causal Reasoning Ability of Large Language Models