If you find any of my observations to be inaccurate, please feel free to let me know…
I appreciate that this study focuses on introducing guardrails & checks for conversational UIs.
When interacting with real users, incorporating a human-in-the-loop approach helps with data annotation and continuous improvement by reviewing conversations.
It also adds an element of discovery, observation and interpretation, providing insights into the effectiveness of hallucination detection.
The architecture presented in this study offers a glimpse into the future, showcasing a more orchestrated approach where multiple models work together.
The study also addresses current challenges like cost, latency, and the need to critically evaluate any additional overhead.
Using small language models is advantageous as it allows for the use of open-source models, which reduces costs, offers hosting flexibility, and provides other benefits.
Additionally, this architecture can be applied asynchronously, where the framework reviews conversations after they occur. These human-supervised reviews can then be used to fine-tune the SLM or perform system updates.