Parsing Expression Grammar vs. Regexes: Building Org Parser in Lisp, Export HTML
Thread
Unofficial Hacker News client; not affiliated with Y Combinator.
Parsing Expression Grammar vs. Regexes: Building Org Parser in Lisp, Export HTML
Unofficial Hacker News client; not affiliated with Y Combinator.
overthenexttwod · · focus · HN ↗
LLMs have changed the equation. It used to take me 2-3 hours to write a parser for a moderately complex grammar. Now, if I hand an LLM a loosely written, BNF-ish grammar and ask for a recursive-descent parser, it finishes the job in five minutes. At this point, writing them by hand is hard to justify. The model does it substantially faster, and lately, often better than I do.
Which makes me wonder: what’s the appeal of PEGs or parser generators now? They used to make sense when hand-writing wasn't practical, but what compelling reasons are left to use them today?
genxy · · focus · HN ↗
How small of a model can complete the operation you described above? If writing a parser still requires a software forge with 2T of vram and a petabyte of training data, then I still see value in PEGs.
Maybe the smaller local models, given a structured grammar can use a PEG to generate a parser.
torginus · · focus · HN ↗
No LLMs needed, and you can jump straight to coding, and you don't have to go through precedence shenanigans.
UncleEntity · · focus · HN ↗
I just had the robots write a PEG parser generator...
Which can do analysis on the grammars which, I suspect, a hand (or LLM) written one can't do so you don't end up chasing infinite recursion, dead rules and whatnot. It also got shoehorned into the regex engine (<a href="https://arxiv.org/abs/1210.4992" rel="nofollow">https://arxiv.org/abs/1210.4992) for my toy Java 1.0 compiler to loop back to TFA.
For my APL interpreter they had to do the 'handwritten' parser (a Pratt parser a sibling comment brings up) as you need to combine parsing and evaluation since there's no way for the parser to tell what it's looking at because APL syntax is just weird.
Horses for courses, as they say.
dig1 · · focus · HN ↗
The reason why it became second nature for you is this hand-rolling, manual work. I observed that with math - unless you manually practice problem solving (integrals, differential equations), you might know the mechanics but you'll have hard time solving them. And that knowledge of mechanics will also fade away at some point.
> LLMs have changed the equation. It used to take me 2-3 hours to write a parser for a moderately complex grammar. Now, if I hand an LLM a loosely written, BNF-ish grammar and ask for a recursive-descent parser, it finishes the job in five minutes. At this point, writing them by hand is hard to justify
So, what is the difference between LLMs generated "manual" parsers vs those with parser generators, beside that with LLM that will be done in five minutes + plus some $$ and with parser generators you'll get that for free and under a second?
> Which makes me wonder: what’s the appeal of PEGs or parser generators now? They used to make sense when hand-writing wasn't practical, but what compelling reasons are left to use them today?
The appeal is predictability - use i.e. Bison, feed it with grammar and you'll get always the same output. Not so much with LLMs.