alt Hacker NewsThere's also two other important limitations to using an LLM and just providing it with pairs of records.
It does not know enough about the records in the context of the overall dataset:
- what is the data quality and to what extent do we expect a errors in some fields - how unusual are certain values such as names in the context of the dataset as a whole, e.g. some names would be very common in some countries but rare in others.
I've written in more detail about this here: https://www.robinlinacre.com/fellegi_sunter_accuracy/
Another great article, thanks. I thought a lot about that too, and basically realized the same thing (this wasn't the coding agent), that frequency in the dataset mattered a lot for how useful a match is, and made frequency lists from my datasets, both on how common the literal names were and the phoneme-reduced names.
But I'm worried about that "bitter lesson" the TypeSafe CEO refers to, and that we're retreading the steps of natural language processing and a lot of other fields, trying to come up with clever rules, when the rule-based approach simply never gets good enough. I need matching to be good, it's absolutely central to genealogy, and I've seen the damage blindly linking by hand-crafted matching formulas can do.