That's nice, I've had the issue where LLMs would return non-existent uids. But does this package actually help with that? Token savings are nice, but not really my main concern. If this can measurably reduce hallucinations, it would be really useful.
> Where UUIDs cost ~23 tokens and get hallucinated by LLMs, id-agent produces memorable word-based IDs at ~14 tokens with equivalent collision resistance.
Yes, we have the validation methods to verify the output. https://github.com/vostride/id-agent/#validateid
A random "-" separated words will fail the validation check.
My gut feeling is that the hallucinations are caused by the entropy. A UUID has unlikely character sequences. But the entropy is a core feature. Turning the UUID into words keeps the same entropy, you just have surprising words instead of surprising hex sequences.
I would be surprised if this actually helped with hallucinations. Happy to be proven wrong though, and this seems like an easy experiment to run: just take a tiny model (below 1B) and have it transcribe a couple thousand ids in both formats, then check where it made more mistakes