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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.20939 |
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| _version_ | 1866916663461412864 |
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| author | Thompson, Horacio Sapino, Maximiliano Ferretti, Edgardo Errecalde, Marcelo |
| author_facet | Thompson, Horacio Sapino, Maximiliano Ferretti, Edgardo Errecalde, Marcelo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20939 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hacia la interpretabilidad de la detección anticipada de riesgos de depresión utilizando grandes modelos de lenguaje Thompson, Horacio Sapino, Maximiliano Ferretti, Edgardo Errecalde, Marcelo Computation and Language 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. |
| title | Hacia la interpretabilidad de la detección anticipada de riesgos de depresión utilizando grandes modelos de lenguaje |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.20939 |