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Comment by applfanboysbgon | Hacker News Reader

Spellchecking is absolutely not a solved problem. I immediately disable spellchecking on every avenue it tries to approach because managing a bunch of dictionaries on every browser/device/application that has its own spellchecker for some godforsaken reason to not have squigglies spammed over every piece of jargon, slang, and slightly atypical spelling is incredibly annoying. I don't know how effective LLMs are, but it's difficult to imagine they can be worse than the existing regime, which is embarrassingly bad for the decades it's been around.

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An interesting idea I saw long ago in some book (I thought it was K&P's "Software Tools," or my second guess was K&R1, but neither of those panned out — a strong Mandela effect) was the clever idea of a whole-document spellchecker that works purely probabilistically, by histograms: you feed it a document, it tallies the trigraphs, and any trigraph that appears only rarely is flagged as a likely typo. This approach lets through unknown-but-realistic words like "antithematory" while flagging unrealistic words like "prisencolinensinainciusol" (because of its unlikely "ciu" and "ius" clusters) and "antthemaory" (because of "ntt" and "aor").

To make this approach work better, feed it a bunch of English text (or whatever language your document is in) before the document you really want to "spellcheck."

Essentially this isn't a spell "checker" so much as a spell "linter" — it looks for antipatterns statistically associated with bugs, and reports the patterns for further investigation.

If anyone knows where this trigraph-based "spellchecker" was first presented, I'd love to find out again.

I like that. Might some of that logic have made it to consumer word processing programs?

That's uhh...a language model?

It's a modeling of language, it's not structurally anything like an LLM.

It’s literally a trigram (character) language model. Check any NLP book from before 2015 or so.

LLMs have more stuff bolted onto them (embeddings, RLHF) but the autoregressive core is a direct descendent of that sort of language model.

So, LM vs LLM? :)

It is structurally exactly the same thing as an LLM...

You used to be able to add your own words to spellcheckers, somehow that went out the window. I rarely see the option for it on a red-lined word now in the context menu, and when it does adding the word seems to make no difference at all.

Human copy editors are less than perfect too. I hired one copy editor who I could not trust to be the last person who touched a document before it went out.

I had a friend who wrote an article for the New York Times: the article made a lot of sense before she submitted it, but it was edited for length and style and it definitely read like a New York Times piece but didn't completely make sense.

Try the LanguageTool. It's now good enough to show smelling pisstakes inside my IDE, including things like missing articles. Without creating tons of visual noise.

You're talking about deployment, not the technical implementation of spellchecking itself.

I have been using reliable spellchecking since the 1980s.

I can agree that LLMs might yield better results overall than a standard spellchecker.

If your goal is to check your writing for plausibility and rough grammatical correctness, that's certainly an open problem for deterministic, conventionally-written software tools.

My goal with spell checking is to make sure my occasional mechanical typos while using a desktop computer get caught before someone else has the chance to be annoyed by them.

I don't have an issue with using the wrong word entirely when writing at a computer, so that's not a use case I think about. It does happen when I use a smartphone, due to autocorrect and predictive typing, but that's not a case this Claude skill applies to.

So, for my use case, the ~6 orders of magnitude more energy used to send documents over the network to be hyperchurned on an array of GPUs guzzling electricity is pure waste.

It also makes the whole process orders of magnitude _slower_.

I find that massive waste and slowdown infuriating, even while conceding that it can perhaps deliver a little more value then the deterministic spell-checking algorithms I rely on.

A problem with LLM-based spell checking is that it can alter the actual meaning of the sentence in its quest to improve the spelling. This is a fairly hard problem to fix.

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