I wonder if telling (or somehow architecturally coaxing) the LLM it has 'skin in the game' will make it more risk-averse? I imagine it does. This makes me wonder too about the entire premise and worthiness of these evals. They orient themselves around normal one-shot interactions with a likely non-sys-prompted model with no built up context or memory of the person. I doubt the mentioned 'job loss' scenario is even co…
The problem I see with this approach is threefold. First, from a technical standpoint the required context window would be massive if you're looking at a person's career/life holistically. Probably solvable, but definitely something to be aware of. Second, privacy goes completely out the window since you're sharing everything. You don't know what's relevant and what's not up front so you need to provide everything. T…
You don't necessarily need to provide everything. Arthur (our AI) is smart enough to see exactly which information it needs to answer a given question. but, yes, the more information you provide the easier of a time the AI will have in answering your question. Arthur doesn't guess. if there is crucial information it needs he will ask for it. it doesn't have to be a Plaid hook up, a csv or even a simple user response is a start.
On your third point — you'd need an outcomes dataset — that's true for traditional ML, but it's not how this works. The normative layer is finance itself (life-cycle theory, tax rules, amortization) implemented as deterministic calculators, with the LLM doing explanation and elicitation. The paper under discussion is sort of the proof: the models already give theory-aligned advice with zero outcome training. The gap it found is input quality and statelessness, not a missing training set.