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Silent reading: 2mins 51 secs

In today's world, where advanced language models like GPT and Claude provide impressive capabilities thanks to bigger training dataset, it's natural to question the safety of our data. Recent incidents involving major entities have raised concerns. This is the case of Facebook, who initially pledged to protect user data but later engaged in unauthorized data sales, or even OpenAI who leaked personal data in others users’s conversations. These worries intensify when dealing with highly sensitive information, such as company or confidential records. Are we truly comfortable entrusting such valuable data to external entities?

Alternatives to Safeguard Data Privacy

To leverage the power of 3rd party advanced systems, our data must be transmitted to their servers. This data includes users questions as well as context data provided as part of the prompt engineering  Obviously despite attempts made to anonymize or segregate the data or the infrastructure, this raises significant privacy concerns. So, if using 3rd party language models in a private and secure way isn't viable, what other options exist? Two potential paths emerge: build proprietary language models, which comes at a huge expense, or embrace the open-source community and contribute to the development of competitive alternatives.

Benefits of Open-Source Collaboration

Major companies, like Goldman Sachs or Inditex, have started to integrate the open-source culture at the core of their digital transformation initiatives. The objective is to leverage the open-source innovation for their tech ecosystem, to remove privacy concerns and to minimize expensive 3rd party software licenses.

In the chatbot ecosystem, many individuals and organizations have already released their models to the open-source community, driven by motivations like recognition or supporting public initiatives. These models offer businesses an opportunity to enhance their operations while contributing to the growth of language models for the benefit of all.

Last but not least, the open-source software provides strong benefits over closed-source solutions, in particular:

• Strong data transparency
• Improved collaboration and support
• No vendor lock-in
• Higher customization capabilities
• Lower TCO

Bridging the Gap Between Open-Source and Closed-Source Models

At ChatFAQ, we recognize that open-source language models are quickly closing the gap with their private cloud-source counterparts. As outlined by LMSYS organization, the output of the different models is a proof of the progress made.

Comparing deeper open-source and private models reveals an interesting dynamic. Open-source models, driven by resource constraints, prioritize efficiency and optimization techniques, leading to the development of highly refined models over time.

Another important factor of NLP/NLG system is the ability to transparently control and  understand the model biais. Obviously something impossible to achieve with private models.

It is anticipated that in the near future, the collective efforts of the open-source community will likely surpass the performance of privately funded models.

Balancing Security and Efficiency

Open-source language models may offer safer, fairer, and more trustworthy options due to their ability to be scrutinized, improved, and held accountable by the broader community. Unlike closed-source models, which provide little visibility into their data or algorithms, open-source models allow for code review and audit, reducing the risk of malicious attempts that may be present in proprietary models.

ChatFAQ aims to simplify the usage of open-source models for the community. We handle the models preparation, benchmark and auditing on a continual basis, and we simplify usual complex installations into a streamlined deployment process, making it easier for others to leverage the capabilities of these models.

This opens up opportunities for small businesses and fosters new, innovative business models.

Open-Source Model Adoption

By embracing open-source solutions, businesses can have greater control over their systems, including the data handling and privacy practices. Sensitive data can be kept within the organization's infrastructure, reducing the risk of unauthorized access or data breaches.

This is the exact mission of ChatFAQ solution that offers the architecture and processes to efficiently utilize open-source models. We respond to the challenges around cloud deployment, the use of GPUs and CPUs servers in different scenarios, and our focus is on optimizing the administration of these resources to ensure efficient performance and cost-effectiveness of generative-AI assistant.

Stay tuned.