Impressive work, but I'm confused on a number of fronts:
- You are serving closed models like Claude with your CTGT policy applied, yet, the way you described your method, it involves modifying internal model activations. Am I misunderstanding something here?
- Could you bake the activation interventions into the model itself rather than it being a runtime mechanism?
- Could you share the publications of the research associated with this? You stated it comes from UCSD.
- What exactly are you serving in the API? Did you select a whitelist of features to suppress you thought would be good? Which ones? Is it just the "hallucination" direction that you showcase in the benchmark? I see some vague personas, but no further control other than that. It's quite black-boxy the way you present it right now.
I don't mean this as a criticism, this looks great, I just want to understand what it is a bit better.
- You are serving closed models like Claude with your CTGT policy applied, yet, the way you described your method, it involves modifying internal model activations. Am I misunderstanding something here?
- Could you bake the activation interventions into the model itself rather than it being a runtime mechanism?
- Could you share the publications of the research associated with this? You stated it comes from UCSD.
- What exactly are you serving in the API? Did you select a whitelist of features to suppress you thought would be good? Which ones? Is it just the "hallucination" direction that you showcase in the benchmark? I see some vague personas, but no further control other than that. It's quite black-boxy the way you present it right now.
I don't mean this as a criticism, this looks great, I just want to understand what it is a bit better.