30 karma · joined February 21, 2020
"Speed, quality, price: pick two". I think I'd rather see a choice like that than the current convolution of $-per-token, sub quota, latency, simulated reasoning level, model size, context size, etc, etc.
> decision models like Jev do not reliably outperform [other] models in speed or accuracy. However, they rightly refocus industry attention on lightweight, task-specific inference paradigm that more closely resembles predictive machine learning
(I also noticed some issues with bolding in Table 4 that downplay Jev's performance a little.)
Unfortunately I think if you made this into a fun multiplayer game, you would just have reinvented Among Us.
- The pythonrepl or llm-math agent not being used when it should be and the agent returning a wrong or approximate answer.
- The wikipedia and webbrowsed agents doing spurious research in an attempt to answer a question I did not ask (hallucinating a question, essentially).
- Agents getting stuck in a loop of asking the same question over and over until they time out.
- The model not believing an answer it gets from an agent (eg using a Python function to get today's date and not believing the answer because "The date is in the future").
When you layer all this on top of the usual challenges of writing prompts (plus, with Python function, writing the docstring so the agent knows when to call it), wrong answers, hallucination, etc, etc, I'm unconvinced. But maybe I'm doing it wrong!