If you are on a sota model and your context window is less than 100k tokens and you don't have any vague or contradicting rules, then I've almost never seen a rule broken
The most common failure I've seen come from tools that pollute their context with crap and the llm will forget stuff or just get confused from all the irrelevant sentences; which if the report is true, is probably what these ai notetakers are guilty of. This problem gets exacerbated if these tools turn on the 1M context window version.
Except you can't be sure it isn't producing nonsense when you do this, and generally the model(s) will be overconfident. This has been studied, see e.g. https://openreview.net/pdf?id=E6LOh5vz5x
> An alternative way to obtain uncertainty estimates from LLMs is to prompt them directly. One benefit of this approach is that it requires no access to the internals of the model. However, this approach has produced mixed results: LLMs can sometimes verbalize calibrated confidence levels (Lin et al., 2022a; Tian et al., 2023), but can also be highly overconfident (Xiong et al., 2024). Interestingly, Xiong et al. (2024) found that LLMs typically state confidence values in the range of 80-100%, usually in multiples of 5, potentially in imitation of how humans discuss confidence levels. Nevertheless, prompting strategies remain an important tool for uncertainty quantification, along with measures based on the internal state (such as MSP).
I've had various agents backed by various models ignore the shit out of various rules and request at varying rates but they all do it.
When you point it out "Oh yes, I did do that which is contrary to the rules, request <whatever>.. Anyway..."
If you are on a sota model and your context window is less than 100k tokens and you don't have any vague or contradicting rules, then I've almost never seen a rule broken
The most common failure I've seen come from tools that pollute their context with crap and the llm will forget stuff or just get confused from all the irrelevant sentences; which if the report is true, is probably what these ai notetakers are guilty of. This problem gets exacerbated if these tools turn on the 1M context window version.
Yeah, that's exactly why I have full confidence in that system, especially for medical notetaking. /s
Except you can't be sure it isn't producing nonsense when you do this, and generally the model(s) will be overconfident. This has been studied, see e.g. https://openreview.net/pdf?id=E6LOh5vz5x
>You can just tell the agent to do exactly that
You can.
It just won't do it.
Seems to work for me
https://chatgpt.com/share/6a06a4c5-d454-83e8-a5b2-c9468f6588...