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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Accesso online: | https://arxiv.org/abs/2409.13748 |
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| _version_ | 1866915384524800000 |
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| author | Doshi, Kartikey Shah, Jimit Shekokar, Narendra |
| author_facet | Doshi, Kartikey Shah, Jimit Shekokar, Narendra |
| contents | We present TheraGen, an advanced AI-powered mental health chatbot utilizing the LLaMA 2 7B model. This approach builds upon recent advancements in language models and transformer architectures. TheraGen provides all-day personalized, compassionate mental health care by leveraging a large dataset of 1 million conversational entries, combining anonymized therapy transcripts, online mental health discussions, and psychological literature, including APA resources. Our implementation employs transfer learning, fine-tuning, and advanced training techniques to optimize performance. TheraGen offers a user-friendly interface for seamless interaction, providing empathetic responses and evidence-based coping strategies. Evaluation results demonstrate high user satisfaction rates, with 94% of users reporting improved mental well-being. The system achieved a BLEU score of 0.67 and a ROUGE score of 0.62, indicating strong response accuracy. With an average response time of 1395 milliseconds, TheraGen ensures real-time, efficient support. While not a replacement for professional therapy, TheraGen serves as a valuable complementary tool, significantly improving user well-being and addressing the accessibility gap in mental health treatments. This paper details TheraGen's architecture, training methodology, ethical considerations, and future directions, contributing to the growing field of AI-assisted mental healthcare and offering a scalable solution to the pressing need for mental health support. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_13748 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TheraGen: Therapy for Every Generation Doshi, Kartikey Shah, Jimit Shekokar, Narendra Computation and Language Artificial Intelligence Human-Computer Interaction We present TheraGen, an advanced AI-powered mental health chatbot utilizing the LLaMA 2 7B model. This approach builds upon recent advancements in language models and transformer architectures. TheraGen provides all-day personalized, compassionate mental health care by leveraging a large dataset of 1 million conversational entries, combining anonymized therapy transcripts, online mental health discussions, and psychological literature, including APA resources. Our implementation employs transfer learning, fine-tuning, and advanced training techniques to optimize performance. TheraGen offers a user-friendly interface for seamless interaction, providing empathetic responses and evidence-based coping strategies. Evaluation results demonstrate high user satisfaction rates, with 94% of users reporting improved mental well-being. The system achieved a BLEU score of 0.67 and a ROUGE score of 0.62, indicating strong response accuracy. With an average response time of 1395 milliseconds, TheraGen ensures real-time, efficient support. While not a replacement for professional therapy, TheraGen serves as a valuable complementary tool, significantly improving user well-being and addressing the accessibility gap in mental health treatments. This paper details TheraGen's architecture, training methodology, ethical considerations, and future directions, contributing to the growing field of AI-assisted mental healthcare and offering a scalable solution to the pressing need for mental health support. |
| title | TheraGen: Therapy for Every Generation |
| topic | Computation and Language Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2409.13748 |