[manbitesdog]

Cool stuff. I'm the CTO of Stargazr (stargazr.ai), a financial & operational AI for manufacturing companies; we started using transformers to process financial data in 2020, a bit before the GPT boom.

In our experience, things beyond very constrained function calling opens the door to explainability problems. We moved away from "based on the embeddings of this P&L, you should do X" towards "I called a function to generate your P&L, which is in this table; based on this you could think of applying these actions".

It's a loss in terms of semantics (the embeddings could pack more granular P&L observations over time) but much better in terms of explainability. I see other finance AIs such as SAP Joule also going in the same direction.

[ashater]

Thank you. Agreed, we are exploring different ways to apply these interpretability methods to a wide range of transformer based methods, not just decoder based generative applications.