Why We Prioritized Transparency Over Fine-Tuning
In the world of AI, fine-tuning is often seen as a magic bullet - a way to quickly optimize a Large Language Model (LLM) for specific tasks. Here at Signpost, however, we took a different approach when developing our LLM. While the option to fine-tune was tempting, we ultimately chose a path that prioritizes transparency and responsible AI development. Let’s delve into the reasoning behind this decision.
Fine-Tuning: A Double-Edge Sword
Fine-tuning an LLM involves training it on additional data specific to a desired outcome. This can be highly effective, but it comes with certain drawbacks:
Black Box Effect: Fine-tuning can make the LLM’s decision-making process less transparent. We wouldn’t be able to fully understand how it arrives at its outputs, hindering our ability to identify and address potential biases.
Limited Control: Once fine-tuned, it can be challenging to modify the LLM’s behavior without significant re-training. This reduces our flexibility and ability to adapt the LLM to evolving needs.
Example of fine tuning using prompts.
At the outset of our development, we prioritized constructing a robust architecture. This wasn’t without reason - a solid foundation is essential for achieving our core objectives:
Rapid Testing and Evaluation: Designed to facilitate swift testing and evaluation cycles. This allowed us to experiment with different approaches, identify strengths and weaknesses quickly, and continuously improve our LLM’s performance.
Connected Knowledge Base: A well-designed architecture ensures a seamlessly connected knowledge base. This fosters consistency and helps prevent information silos, where the LLM might struggle to access relevant data.
Relevance and Safety: The architecture plays a critical role in ensuring the LLM’s knowledge base is both relevant and safe. It allows for effective data management, reduces the risk of bias, and promotes the delivery of accurate and trustworthy information.
At Signpost, we believe transparency is fundamental to earning trust in AI. We want to understand how our LLM works, not just what it produces. By opting out of initial fine-tuning, we gain several advantages:
Clearer Explanations: Understand the reasoning behind the LLM’s outputs.
Identifying Biases and Hallucinations: A less fine-tuned LLM may reveal underlying biases in the training data. This transparency allows us to address these biases proactively and ensure our LLM operates fairly.
Responsible Development: This approach allows for ongoing evaluation and refinement as we learn more about the LLM’s capabilities, where we can adapt and improve our technology with ethical considerations at the forefront.
Fine-Tuning Isn’t Off the Table
This initial decision doesn’t mean fine-tuning is out of the picture. As we gain a deeper understanding of our LLM and its strengths and weaknesses, fine-tuning will allow us to experience its magic, the possibilities of altering the outputs, and have fun with it.
At Signpost, choosing not to fine-tune initially wasn’t a detour; we chose Rawness over Refinement, it was a deliberate path forward to prioritize a “blank slate” approach for Transparency First, Stronger Foundation, and Flexibility for the Future. We have a clear understanding of our LLM’s core abilities and a solid foundation for future enhancements.
