Comment by jwpapi
20 hours ago
Germans write dates like 17.02.2026
It’s an interesting problem. And i have following thoughts to it?
- Are all cases even decidable?
- Shouldn’t one false positive rank higher than a hundred rank negative.
-Shouldnt’t the AI just abstain from decisions to avoid false positives.
- Is it smarter to seperate this in a lot of small sub problems and to define this as a rule set of small yes no decicions, why does this need to be solved agentically?
- You should definitely clean up deterministically first.
I feel like if you really want to solve this problem, you would approach it differently. This seems like a wrong approach and a bad article on top?
> - Are all cases even decidable?
No, and I think undecideable is quite frequent.
> -Shouldnt’t the AI just abstain from decisions to avoid false positives.
Yes, and I think regulation should be quite strong in this regard.
> - Is it smarter to seperate this in a lot of small sub problems and to define this as a rule set of small yes no decicions, why does this need to be solved agentically?
For larger contexts I worry about agentic confabulation, but humans unquestionably look at the gestalt when doing matching. I don't want idiot clankers making enforced decisions because they have misunderstood one field.
> - You should definitely clean up deterministically first.
If you can't do field validation on entry then I'm not sure deterministic rules work better, they may be worse, although easier to reason about.
But surely there are some deterministic reasons to clean up.
I personally wouldn’t lean towards LLMs on identity resolution. I would like give it the dataset and then ask me questions about specs? I really struggle to find the LLM part reasonable here. I mean potentially some typos, or nicknames, but those could be potentially resolved deterministically also.
I would probably have reviewer running that suggests new rules that I could implement. To me this seems more like compiler work than LLM work.