arXiv:2606.29857v1 Announce Type: new Abstract: Chatbots have become increasingly prevalent across various domains, offering automated assistance in many areas, especially mental health support. The training is done using extremely large datasets, which are sometimes not available in very specific domains. Moreover, it would sometimes be ideal to train the chatbot with personal information about the patients, which, of course, cannot be done on shared servers since it would violate patient confidentiality. Hence, being able to improve the performance of a chatbot, possibly trained locally and
The increasing prevalence of chatbots across sensitive domains like mental health support, coupled with growing concerns over data privacy and the limitations of large, centralized datasets, necessitates innovative training approaches.
This research explores a method for enhancing chatbot performance with smaller, potentially local datasets and personal information, directly addressing critical privacy and data accessibility challenges in AI development.
The ability to improve chatbot performance without relying on massive, shared datasets could decentralize AI training and enable more privacy-preserving applications, particularly in sensitive sectors.
- · Healthcare providers
- · Privacy-focused AI developers
- · Individuals seeking personalized AI services
- · SME AI companies
- · AI companies reliant on centralized large datasets
- · Cloud service providers (potentially, for certain use cases)
- · Traditional, data-intensive AI training methods
Improved performance of chatbots in specialized and sensitive domains without compromising data privacy.
Increased adoption of AI tools in highly regulated sectors due to enhanced data security and localized training capabilities.
A shift towards more privacy-centric and distributed AI model development paradigms, reducing dependency on giant datasets and centralized infrastructure.
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Read at arXiv cs.LG