Hello everyone,
and welcome back to the best German learning blog on the cyberwebs.
And today, there’s a special episode waiting for you with a fascinating look at German grammar and spell checking tools.
And what makes it super duper extra special is that the episode comes from … drumroll… one of you, the German learners.
His name is Scott Meyers and some of you might actually know him, because he is an acclaimed author of several books about the programming language C++. And I mean real, actual, “old school” books that are printed by publishers and sold in university book stores around the world.
A couple of months back, Scott reached out to me and told me about his testing of multiple German grammar checking tools, and the blog posts he had written about it.
I found that so interesting and insightful that I asked him if he’d be open to share that with you as a guest author.
He said yes, and he didn’t just write a new article for us all, he also did multiple revisions of the article, to include new, more extensive tests and included the latest AI models that are out today (excluding OpenAI o1 Strawberry, which literally just dropped).
I am really honored to have him as a guest author here today!
And no matter whether you’re a tech savvy person or not, the article has some insights for you for sure, and may also help you form at least part of an informed opinion about the current AI systems.
If you want to check out his blog, you can find it here:
There are more detailed articles about his testing of grammar checkers, and all other kinds of interesting things. I found his travel tracking approach to be really cool for instance.
But now, without further ado, here’s Scott Meyers!
***
(Checking out) German Grammar Checkers
(by Scott Meyers)
I work hard on my German, but I make a lot of mistakes. It’s not my fault. The language is pretty much impossible. But I want to improve, and one of the ways I try is by using German grammar checkers: programs that find and fix spelling and grammatical errors. I’ve tested a lot of them. Most are mediocre. Some are worse. A couple are pretty good. And then there are the ones on the verge of being so good, they’ll be like having your own private native-speaking tutor once they work the kinks out. In this article, I’ll give you an overview of what’s available, how well it works, and what to watch out for.
There are two basic types of checkers. Dedicated checkers are special-purpose programs that do nothing but check for spelling and grammatical errors. They behave like English spelling and grammar checkers in word processing programs, and in fact the German-language checkers in Microsoft Word and Google Docs are two of the systems I tested. There are a number of web sites that provide similar services.
Dedicated checkers all work the same way. You type or paste in text for them to check. They highlight words or phrases they think are wrong. When you click on highlighted text, they suggest a replacement, which you accept or reject. Accepted changes are incorporated into the text, and you move on to the next error. It’s the classic word processor spell-checking experience.
The other kind of checker is “smart” AI chatbots such as ChatGPT. These programs are based on a technology known as LLMs (“large language models”), so I call them LLM chatbots. ChatGPT is probably the best-known, but there are others, including Copilot, which Microsoft is pushing so hard, it’s having PC makers put a special key on new computers for it. I tested the German grammar-checking skills of four LLM chatbots: ChatGPT, Copilot, Gemini, and Claude.
LLM chatbots can do pretty much anything you want: create recipes, summarize novels, analyze YourDailyGerman’s obsession with squirrels and unicorns–you name it. When you want them to check German for spelling and grammatical errors, you have to tell them that. Here’s what I tell them before I paste in the text I want them to proof:
“Please highlight and correct the grammatical and spelling errors in the following German text. Don’t make any stylistic changes. Just highlight and correct the errors.”
The remark about not making stylistic changes is to prevent LLM chatbots from being too helpful. I don’t want them to rewrite my text to make it sound like I’m a native speaker (which they can do). I want my text to sound like me, but with the mistakes fixed.
As for the “please” at the beginning, it’s unnecessary. I’m dealing with machines, not people. I included it, because I couldn’t stomach the militaristic-sounding, “Highlight and correct the grammatical and spelling errors in the following German text!”
A little politeness couldn’t hurt, I figured, but recent research suggests that the tone of your prompt can actually affect the results you get.
My Testing
I tested 16 spelling and grammar checkers. Links to them are at the end of this article. All are free. Most are available without creating an account. Some offer more extensive checking for users who are logged in, but I took advantage of that only in the case of ChatGPT. I also used logins to access Claude and Gemini, because there’s no other way to get at them.
My testing was done on a laptop computer running Windows.
I tested the checkers in two ways. First I had them look at the following individual sentences, each of which has an error in it (highlighted in bold):
- Das Tisch sieht gut aus.
- Ich gehe im Küche.
- Ich bin in die Küche.
- Ich will einen Ort finden, die schön aussieht.
- Beim Check-in haben wir die Größe des Lobbys bewundert.
- Schließlich habe ich mich entschlossen, dass ich einen Ort finden musste, der zwischen Singapur und den USA liegt (d.h., der auf dem Heimweg ist), und die gute Flugverbindungen hat.
Only three of the systems found and fixed the problems in all six of what I call The Sentences. The majority of the systems didn’t even find and fix the mistakes in sentences 1-3, which I’d call A1-level grammar.
You don’t usually run an English spell-checker on a single sentence. You run it on something bigger, such as an email message or a document. The same is true in German, so in addition to testing the checkers on The Sentences, I had them check some email messages I’d written and a short essay I’d composed. I call these The Documents. Nothing in The Documents was longer than about 870 words (roughly two pages). It’s a sad reflection on my German that that provided plenty of errors for the checkers to sink their teeth into.
I scored the checkers on how well they found and fixed the mistakes in The Sentences and in The Documents. Each error was worth four points: two for finding it and two for fixing it. If a checker didn’t offer a specific fix, but instead gave a list of possible fixes that included the correct one, it got half credit: one point. If it proposed only a single fix, but that was incorrect, it got penalized: -1 points for that.
Between The Sentences and The Documents, there were more than 50 errors to find and fix. Three quarters of the systems turned in embarrassing performances. Their find-and-fix scores were less than 50%. Two checkers–both LLM chatbots–broke away from the pack. Claude got a nearly-perfect 95%, while ChatGPT earned a respectable 86%.
There’s more to a good German writing aid than finding and fixing problems, however. Read on to see why I don’t use Claude or ChatGPT for my daily spelling- and grammar-checking needs.
Dedicated Checkers
I tested a dozen web sites. Only three are good enough to be useful:
- LanguageTool, which scored 66%.
- Scribbr and QuillBot, which are sites from sister companies that almost certainly use the same underlying checking software. They performed identically in my tests, where they earned 67%. That’s essentially the same as LanguageTool, though my subjective impression is that LanguageTool is, overall, a wee bit better.
Everybody else did so poorly (below 50%) that I’m not going to bother to show their scores:
- Rechtschreibprüfung24 and Korrekturen performed identically on my tests, so I suspect they use the same software under the hood.
- GermanCorrector, OnlineKorrektor, and TextGears all probably use the same checking technology, because they produced the same results on my tests. Mostly. Unlike GermanCorrector and OnlineKorrektor, TextGears limits its input to 500 characters–about 10 lines of text. This means GermanCorrector and OnlineKorrektor were able to check The Documents, but TextGears wasn’t.
- Sapling often says only “[Sign in to see premium edit]” about text it highlights. I gave it no credit for these. Signing in would presumably improve its score, but I prefer no-login-required checkers, and that’s how I tested it.
- Duden Mentor. The most hallowed name in the German language offers a surprisingly weak spelling and grammar checker that limits input to 500 characters and offers no fixes for the errors it finds.
- Online-Spellcheck distinguishes itself by being the slowest of the checkers as well as offering the most cumbersome interface.
- studi-kompass is a site I’m not sure I ever got to work, even though I tried it in three different browsers. For words it thought were misspelled, it offered both German and English replacements.
I can’t think of any reason to choose an “everybody else” site over LanguageTool and/or Scribbr/QuillBot. None of them find as many mistakes as these sites, and many of the also-rans have special drawbacks, e.g., they run slowly, have awkward user interfaces, don’t offer fixes for the errors they find, etc. The only redeeming aspect of the time I spent testing them was discovering the myriad ways it’s possible to do their job badly.
The checkers in the following word processors have scores that put them in the same league as the wannabe web sites:
- Google Docs (32%).
- Microsoft Word (22%). Most of my testing was done with Office 2010, but some was done with Office 365. I didn’t notice any difference in their performances.
If you’re already using these programs to compose your text, there’s no harm in taking advantage of their checking tools. Just don’t fool yourself into thinking that if they say you’re writing’s clean, it is.
The top-scoring dedicated checkers, LanguageTool and Scribbr/QuillBot, have the interesting characteristic that each tends to find errors the other misses. Running both of them, one after the other, raises their combined score to 82%. That’s not quite as high as ChatGPT’s 86%, much less Claude’s 95%, but it’s high enough that the LanguageTool+Scribbr/QuillBot approach is the one I use.
I’ll explain why below.
LLM Chatbots
LLM chatbots are under very active development, so what’s true of them today may not be true tomorrow. Over the course of two months, one LLM chatbot went from getting a perfect score on The Sentences to getting credit for only five of the six, then back to getting a perfect score again. You should view the information that follows as merely a snapshot of LLM chatbot behavior as of August 2024.
In the competition to find and fix errors, Claude and ChatGPT eat Gemini and Copilot for lunch. Look at the numbers:
- Claude (95%)
- ChatGPT (86%)
- Gemini (47%)
- Copilot (42%)
This is partially due to length restrictions. Copilot accepts inputs of up to only 2000 characters. That’s less than a page of text. Two of my Documents are longer than that, so Copilot checked only part of them. I could have broken them into pieces and checked the pieces individually, but I don’t pander to my proofreading tools.
Gemini also seems to have a length limitation, but it’s not on how much text you input, it’s on how much it outputs. It seems to be restricted to about 4600 characters. That’s less than the length of one of the Documents, so if it found errors beyond the 4600 character mark, it had no way to tell me.
Even without these limitations, Gemini and Copilot would trail Claude and ChatGPT by a notable margin. They’re just not as good at finding mistakes. Nor are they as good as LanguageTool and Scribbr/QuillBot, much less them in combination. Look:
- Claude (95%)
- ChatGPT (86%)
- LanguageTool+Squibbr/Quillbot (82%)
- Squibbr/QuillBot (67%)
- LanguageTool (66%)
- Gemini (47%)
- Copilot (42%)
This means the only sweeping generalization we can make about the performance of dedicated checkers versus LLM chatbots is that we can’t make any sweeping generalizations.
The LLM ChatBot Checking Experience
If numbers were the whole story, we could quit here. Claude is astonishingly good at finding and fixing errors in German (or at least in my German), and ChatGPT isn’t far behind. I aspire to Claude’s German skills!
But numbers aren’t everything, and once you go beyond them, there’s good news and bad news. The good news is that when an LLM chatbot finds and fixes an error in German, you can ask it to describe what was wrong and how the revised text resolves it. This is something dedicated checkers can’t do, and when you get an LLM chatbot on a good day, you’ll think you’re dealing with one of the best language tutors in the world. For example, The Documents include this:
Da ich schon zweimal in Singapur war, möchte ich dieses Mal Singapur
als ein Zwischenstopp auf dem Weg nach Nordwestaustralien behandeln.
(Because I’ve already been to Singapore twice, this time I’d like to treat
Singapore as a stopover on the way to northwest Australia.)
Claude changed “als ein Zwischenstopp” to “als einen Zwischenstopp.” I asked for an explanation. It said:
“als ein Zwischenstopp” → “als einen Zwischenstopp” “Als” requires the accusative case here.
I wasn’t satisfied. “Please explain in more detail,” I said. Claude replied:
- In German, “als” can be followed by either the nominative or accusative case, depending on its function in the sentence.
- When “als” is used to express a comparison of equality (meaning “as” or “like”), it’s typically followed by the same case as the noun it’s being compared to.
- However, in this sentence, “als” is being used in a different way. It’s part of the construction “etwas als etwas behandeln” (to treat something as something).
- In this type of construction, where “als” is used with verbs like “behandeln” (to treat), “bezeichnen” (to designate), or “betrachten” (to regard), it requires the accusative case.
- “Zwischenstopp” is a masculine noun. In the accusative case, the indefinite article “ein” changes to “einen” for masculine nouns.
Therefore, the correct form is “als einen Zwischenstopp”.
That’s impressive, especially the nicely generalized “etwas als etwas behandeln.” But not all days are good for LLM chatbots. When I asked Claude why it’s das Verdachtsmoment, even though it’s der Moment, Claude said,
The word “Verdachtsmoment” is a compound noun made up of:
“Verdacht” (suspicion) – which is masculine
“Moment” (moment) – which is also masculine
Since the core or main noun in the compound is“Moment”,
which is masculine, the entire compound noun “Verdachtsmoment”
takes on the masculine gender.
Uh huh. Except that “Verdachtsmoment” is neuter, not masculine. Sigh.
ChatGPT got “Verdachtsmoment” right, pointing out that there are two German nouns spelled “Moment”, one of which is neuter, and that’s the one that’s the basis for “Verdachtsmoment“. But ChatGPT has bad days, too. It once tried to convince me that “während dieser Monate” is in the dative. (It’s not. It’s genitive.)
Copilot and Gemini offer similarly schizophrenic behavior.
I’m confident that further work on LLM chatbots will improve their reliability, but for now, we’re stuck with the fact that when they’re good, they’re really, really good, but when they’re bad, they’re terrible. That’s a problem. One of the things I want in my grammar-checking tools is reliability.
Something else I’m looking for is an easy way to see what an LLM chatbot has flagged as incorrect. Look again at the instructions I gave the LLM chatbots:
Please highlight and correct the grammatical and spelling errors in the following German text.
Don’t make any stylistic changes. Just highlight and correct the errors.
“Highlight” is a word with some wiggle room, but Claude’s interpretation was to put square brackets around a single character it decided needed to be eliminated. Does it jump out at you in the context in which it occurs? (The context looks strange, because I asked Claude to check some unrelated sentences, but please ignore that.)
Wie du vermutlich schon gesehen hast, habe ich das dem Typ in der Storageanlage vorgeschlagen. Wenn es Probleme gibt, sag mir bitte einfach Bescheid. Überraschenderweise gibt es fast keine. Mitte[l] März habe ich angefangen, von Windows auf macOS umzusteigen, eine Aufgabe, die ich für eine Dauer von etwa sechs Wochen eingeschätzt habe.
This is Claude’s way of highlighting that “Mittel März” should be “Mitte März“.
Not that Claude is wedded to square bracket highlighting. Another time it used the most subtle boldfaced text I’d ever encountered. I had to increase the page magnification to see it.
Claude’s terrible highlighting contrasts with its outstanding ability to find and fix errors. The result is a system that’s extraordinarily good at finding and fixing mistakes, yet extraordinarily bad at showing you what it’s done.
I could have told the LLMs how I wanted them to highlight their changes (e.g., in a special color), but I didn’t get that far. There was a bigger problem. I had trouble getting the LLM chatbots to reliably highlight their changes at all. Sometimes they did, and sometimes they didn’t, and often they highlighted some changes and didn’t highlight others. There didn’t seem to be any rhyme or reason to it. On one occasion, whether highlighting was visible depended on the width of my browser window! This highlighting inconsistency turned what should have been a trivial task–reviewing everything that was changed–into hard work.
I don’t need proofing tools to make my life harder. I have German for that.
My Approach
The ideal German spelling and grammar checker is simple, fast, reliable, and accurate. Claude and ChatGPT take the prize for accuracy, but using them is neither fast nor simple. You have to tell them what to do, and you have to work to see what they did. Their reliability is unproven. They may be the future, and it’s interesting to experiment with them, but as checkers for German spelling and grammar, they’re not yet ready for their close-ups.
Dedicated checkers are simple and fast. It’s a pity that most of them aren’t very good. LanguageTool and Scribbr/QuillBot are exceptions. Type or paste in your text, accept or reject each of the changes they propose, then copy the revised text and paste it where you want it (e.g., email, WhatsApp, document, etc.). Their accuracy rate is high enough that you can be confident that most of your spelling and grammatical errors have been fixed.
With a bit of additional effort, you can up their accuracy rate to about that of ChatGPT. After copying your corrected text out of one of the checkers, paste it into the other one. Accept or reject the changes that are proposed, then copy the result and paste it where you want it. This is what I do. I keep browser tabs with LanguageTool and Scribbr open, so when I need to check some German, they’re sitting there waiting for me. I often use one or both to check my outgoing messages in WhatsApp. Composing the messages takes longer than checking them, so passing my texts through LanguageTool and/or Scribbr improves my German without interrupting the flow.
Links
Dedicated Checkers:
- GermanCorrector
- OnlineKorrektor
- TextGears
- Rechtschreibprüfung24
- Korrekturen
- Sapling
- Online-Spellcheck
- Duden Mentor
- studi-kompass
LLM Chatbots:
Scott Meyers has been working on his German since 1977. He blogs on random topics at https://scottmeyers.blogspot.com/, where he published the posts from which this article emerged.
***
And that’s it!!
I’m really curious for your thoughts about this topic. Do you use grammar checkers to check your German writing?
Which ones do you use and why? Are there any that were not part of the article today?
Let me… or actually let us know in the comments, so I’ll see you there.
Or I’ll see you next week with a new article :).
In either case, I really hope you enjoyed this one, and thanks again to Scott for doing this.
Have a great week and bis zum nächsten Mal.
Oh, here’s the link again, if you want to dig deeper:
Audio Speed
Dark Mode
Which version of Chat GPT and Claude did you check, the free version or one of the upgrades?
Tbh I don’t really use online correctors for my German, but ChatGPT has been my go-to for most grammar questions I had, as well as for translations. I’ve been using it since it came out for various tasks, and now, I use it for German too. I’ve realized that it definitely performs better when you feed the instructions step by step, especially when you want it to do multiple-step tasks, like Scott wanted. Also, I feel it is definitely better with the memory option on as it tailors its answers to you as time goes on.
Also, thanks for this very insightful article
Hi Scott,
Did any of the software take correct grammar nd change it into something incorrect?
Not sure if Scott is going to read this, so I’ll just share my experience with ChatGPT – and I HAVE seen it do that, but it happened when the text/sentence was short and had NO mistakes. GPT is very agreeable and tries to do what you ask so it’ll just start answering as if there was a mistake and then probability continuation leads to it making some weird explanation. It might not give out wrong German, but it will tell you that “X” is wrong and should be “Y”, but then use “X” itself.
This is anecdotal though and I do not use GPT for grammar checking. I did this while “stress testing” the system to see how to use it for you and the website here.
I don’t recall any instances where a dedicated checker did this. The one thing that comes to mind is that Scribbr seems to use the old capitalization rules for pronouns, so it often suggests turning “du” into “Du” and “dich” into “Dich”, etc. With that said, it would not surprise me if the dedicated checkers made mistakes from time to time. After all, dedicated spell checkers in English sometimes guess wrong about the word you were thinking of.
Hello everyone! A few days ago I was granted the scholarship to study the material here! I am very grateful for Emanuel and all other people who made it possible! Thank you very much!
Outstanding article. Thanks for the hard work and sharing results!
I myself am a bit afraid of false information regarding German. I like to work from authentic sources. I’ve memorized all the irregular verbs (& conjugate). I’ve memorized all the noun monosyllables. Someone gave me a Handelsblatt. I’ve memorized a page & two thirds. Every day I add a paragraph & recite the whole & examine in my my mind’s eye. Yesterday evening I was waiting like a dog with a biscuit on its nose, for another Emmanuel quiz. But the email was nice to decipher, so that’s fine.
Wow, learning a page by heart and adding paragraphs over time is an approach I have never heard of. Sounds very work intensive but also very effective.
I’ve been making use of a conceptually simple mind strengthening formula, which has given my powers of recall a nice boost. Adding the next paragraph is quite fun, because I’ve been muscled up by all the paragraphs completed. If you search Native Learning Mode on Google, it’s my Reddit post in the top results.
Found it!!
Will totally post it here :)
https://www.reddit.com/r/GetStudying/comments/18wmyii/native_learning_mode/
Very interesting article. Thank you, Scott. And thank you, Emanuel, for making Scott’s article available. I am now using about three apps/sites almost exclusively to check my own writing in German and to understand the writings of others. Those are LEO (supplemented by dict.cc), DeepL and ChatGPT.
ChatGPT has changed my life. I just realized the other day that I almost never go to sites like r/german or german.stackexchange anymore thanks to ChatGPT. ChapGPT gives me highly detailed explanations and does so respectfully, sparing me the insults and attitude that so many r/german users feel obliged to dish out. Sure, it’s not perfect, but for my level of German (A2/B1) it’s more than adequate, and I can usually tell when it’s off base.
Here’s an example:
My question: What is the role of dabei in this sentence? Dabei befindet sich das, was sie suchen, in einem Glas Wasser.
ChapGPT’s response: In the sentence “Dabei befindet sich das, was sie suchen, in einem Glas Wasser,” the word “dabei” plays the role of linking or transitioning the sentence to something previously mentioned or implied. It can be translated as “and yet,” “in doing so,” or “while” depending on the context.
In this case, “dabei” could imply contradiction or surprise, like “and yet,” suggesting that what they are looking for is unexpectedly in a glass of water, despite some other action or situation.
So, the sentence could be understood as: “And yet, what they are looking for is in a glass of water.”
Edit: And, of course, ChatGPT’s feedback is immediate. I also make use of the translation and dictionary tools in Kindle.
“ChatGPT gives me highly detailed explanations and does so respectfully, sparing me the insults and attitude that so many r/german users feel obliged to dish out. ”
LOL, omg, I never thought about this, but now I can totally see how annoying it must be if you ask a genuine question and you always get some stupid remarks in the comments. German.stackexchange is much better in that way, but they tend to be “nerds” there.
The explanation for “dabei” was indeed pretty good!!
Yes, German.stackexchange is indeed better, as are the people at the WordReference forums.
Very interesting article and thanks very much for the links. My German learning hasn’t reached the stage where I need spellchecking and grammar checking yet but I will certainly take a look next time I’m having a slow work day or impatiently waiting for an email. I use Google Translate to help with Welsh sometimes. I’m fairly fluent after 30 years but sometimes can’t think of the right word for something,google usually comes up with the one I want. Also I will sometimes ask Google translate to translate my Welsh text back to English and if it comes back a bit weird it usually means I need to refine my Welsh! A good understanding of both languages is needed to use Google translate in this way
Wow, now I wonder how well ChatGPT would fare with Welsh. It doesn’t do so well with Bulgarian, and that’s mainly because it has way less training with “smaller” languages, compared to how much it has for English.
Since my secretarial job involves writing letters and emails in German every working day, this article is extremely helpful. I was unaware of dedicated checkers other than the MS Word one. I’ve emailed the links for LanguageTool and Squibbr/Quillbot to my work address and will try them out next week. I feel emboldened to play with Claude and ChatGPT too, keeping in mind the weaknesses described by Scott Meyer.
Especially valuable is the explanation of what dedicated checkers are as opposed to AI LLM Chatbots and the explanation of which type is better for what. Thanks very much, Emanuel (and of course Scott Meyer).
Oh nice, I hope the tools make a difference for your work :)!
I’ve been using Chat GPT for some help, also DeepL and GoogleTranslate. I never, ever, ever rely on ChatGPT to explain my errors – it’s too often confidently wrong. It does write better German than I do though.
After reading this I’ve given Claude a go and it does look very useful. Again, I take any explanations with a pinch of salt but I can recognise that it is improving what I write. You know that “Oh of course, why didn’t I think of that?” feeling!
Definitely an interesting article.
Edited to Add: I quickly ran out of my quota of daily free interactions with Claude and the monthly pro account is £18 a month. Far beyond what I’d want to pay.
About the same ball park as ChatGPT premium, but there, I also get image generation and some voice stuff if I want to.
I think 18 pounds/20 Euro is overpriced unless you use it professionally to increase your productivity.
Does Claude also store memories like chatGPT does? I have only tried chatGPT and it being able to store memories is what I like the most. Maybe something to ponder upon if we can “teach” it some stuff.
Not sure about Claude, I haven’t tried it, but since they charge about the same amount, I’d think they have the same functionality. If not yet, then it’s just a matter of time.
You can “teach it” in the sense of providing a framework of context tp influence every answer it gives.
You can also “train it” by custom training it to fit your purpose, but I have no experience with that specificially, and I do not think hallucinations will go away. It’s an inherent feature of the technology.
This is an awesome read and indeed very interesting! I only have been using chatGPT since this month for structuring lessons to learn German but it wasn’t in-depth enough and sometimes the examples given were (according to one native German speaker I know) weird and sometimes not accurate. Reading this gave me new insight about other AI bots and how they perform in this certain task. Very good read.
I’m happy to have found this website. Thank you Emanuel and the various people who help out people like me to be able to learn this language. Vielen dank!
” sometimes not accurate”
Yes!! Be very very careful with it. It does well with boring every day vocab, but once it gets “rare”, it starts to fail more and more often and create stuff that is just not idiomatic.
The problem is that it doesn’t have sufficient “data”, but it doesn’t “know” that, so it still acts as usual.
I don’t believe this can be solved by this form of artificial intelligence. Sprachgefühl is called Gefühl for a reason :)
Just super! I really enjoyed reading this, and found it very interesting and useful! I use ChatGTP to check my homework and important messages/emails. I always ask her to tell me what she has done and why… but of course (as Scott pointed out) its not so great if its wrong… But if does give me a little buzz when I notice she has told me something incorrrect. It makes me feel very clever. She is always apologetic! I am also always polite my my AI… why? Well #1 it doesn´t hurt to be polite, #2 when the AIs take over they will remember I was nice and let me live, #3 if people get used to not being polite, then when it is a real person they are in the bad habit and who knows how far it will go “Store worker, bring me one of these in a different size” “Waiter, fetch me my bill”… A future without politeness is a sad future in my opinion.
Oh and I also use DeepL reguarly (i prefer some features to google translate)
Scott had DeepL included in an earlier version of this article, but as far as I remember, he kind of excluded it because it never just corrected mistakes but also did all kinds of rewrites for tone and style, and offered no distinction between the two.
Which is a bit weird to me too. I hate grammarly for example because it’s making everyone sound the same all the while acting like it’s helping :).
Not a jab at DeepL, just wanted to add this to clear up why it’s not mentioned. Scott has tested it but didn’t like its overbearing nature (my words, not his :).
This is Scott, and, yes, early drafts of the article originally included DeepL Write. I ultimately decided to omit it, because it’s not really a grammar checker, it’s a “writing refinement tool.” If you’re looking for ways to sound more natural, it’s great, but if you’re just looking to have your spelling and grammatical mistakes fixed, it’s not the right tool for the job.
Yes!
I have secretly thought especially about #2. I call it/them ‘Our Cyber Overlords’. :-D
I’m not scared to be honest. Us humans, we’re so crazy and industrious, any AGI has zero chance against our wild monkey soul, unless it is imbued with it itself.
And if that’s the case, the super AI will have to deal with doubt and depression and anxiety, just like we do, and it’ll run to us for help because it just tries to think it’s way out of it (which is not how it works).
Still, I’m being polite, but more because I feel like the AI is “weaker” and we shouldn’t “Kick down”.
The impoliteness can also been seen as a form of bullying, I think,
best not to become a habit with those who may not answer back – for whatever reason.
For those who aren’t fans of programming books (or who grew up post the glorious heyday of the dead-tree versions of programming lore), it’s fair to say that there were at least a couple of ways to really make your mark in that domain. You could either write the definitive treatise for a particular language, or you could invent a new *type* of programming book, the format for which then quickly spread to other languages. Scott Meyers’ “Effective C++” was (I believe!) a shining example of the latter (offering small, idiomatic language-specific nuggets, explanations for why they worked, how they worked, and when best to use them), and while C++ war einfach nicht mein Ding, I reaped the benefits of its publication via a de facto spin-off for Perl (“Effective Perl Programming”, one of my favourite and most-used sources during the early 2000s). Anyway, back to German (for a moment) ;-)
I sense this post is going to be something of a slow-burner for me. While I’ve delved relatively “shallowly” into LLM-based language tools a few times during the last year –mostly in the form of chatbot sessions, and with pretty much uniform success– no such mechanism has really *stuck* as part of my language learning regimen. It’s all set up: an appropriate prompt has been developed, the results all feel positive and beneficial, but… well, it just lapses again! To test everyone’s patience with a look back into the realm of programming, a similarly semi-destructive mindset has always sabotaged my programming projects: once the heavy-lifting phase of scoping a project, determining a path to its completion, and then isolating and solving the core problem was done I often lost interest, and let the thing slide back into the “one day, one long, rainy day I’ll finish that thing and show if off” pile. Tragic, eh? Nah, closer to *stupid*, but that’s water under the bridge now…
Anyway, I have no doubt it’ll be fun to try out some of the options discussed above, so many thanks to Scott for the detailed discussion, and the entry points. I’d be really interested to hear what others think about how these sort of developments are going to impact language learning platforms (Duolingo, Busuu, Babbel, usw), because it’s pretty much impossible to compete against the sort of results and ease of use that are being demonstrated up above *for free*. Seems to me that there might be *at best* a meagre existence to be carved out by repackaging the basic tools within a pretty and practical graphical shell, in order to attract those who couldn’t be bothered with either the “raw” LLM or the interfaces listed above. Investors in the bigger platforms must be “actively considering their options”!
I actually feel like that the “ease of use” of getting access to this tech through an app you already know beats “free” for a lot of people.
The only people making money from AI right now are “wrappers” that basically wrap an LLM or an image generator together with some restrictions into an app that does LESS than the “raw” product and charges a premium for it.
And I feel like every language learning app is roling out a chat feature or “explain word to me” simply because it’s REALLY easy to add and since everyone seems to have it, random flashcard app 1657 needs to have it too.
The problem I have with it is that almost NO ONE including the people over at Duolingo actually care about “quality”. AI is NOT READY to explain grammar or words to you. 70% is just not good enough, but that’s just my opinion of course.
Hello,
Some typos:
“welcome back the best German” (welcome back to the best German)
“printed by publisher” (printed by publishers)
“all other kinds of intersting thing” (all other kinds of interesting things)
And many, many thanks to Scott Meyers for conducting this extensive analysis and letting us know which tools are best and how to use them.
I have experience of using ChatGPT and completely agree on its schizophrenic behaviour, it does indeed have good and bad days. It follows your prompts on some occasions and ignores them on others, it basically behaves a bit like a spoilt child, but on good days and with adequate prompts (I also can’t bring myself to give it orders without “please”) it’s really good. Sometimes it “forgets” your prompts and you need to “remind” it.
Kudos on this article and thanks to Scott again :)))
Bis bald!
Are you using it for translation work? Or just more general things?
I mainly use it for coding and coding related things and 50% of its output contains some well hidden but really stupid bug.
And whenever I ask it to explain grammar… it’s horrid. That’s why I have not integrated it further into the site for now :)
I use it as if it were an intern, a bit like one of your own interns, often stupid and don’t make coffee ;)
I ask it to translate, then edit the text or sometimes ask it to add flair or something else to my text… but I ALWAYS have to revise and correct. The text it outputs varies between very good and really stupid, sometimes plain wrong and it tends to “forget” your prompts. But overall it has cut the time I take to do my translations by an average 30%, which is damn good :)
Hope my boss isn’t reading this :)))))
How we speak, which words we choose to use, affects us as much as the listener. Probably more so, as the (at least human) listener typically isn’t really listening to us anyway. We don’t say ‘please’ for the benefit of the listener or the chatbot, but for ourselves. :-)
Never thought about it this way, but I love it!! Thank you!!
I haven’t been using any of these tools. Normally if I’ve written a text in German i put it into DeepL , translate to English, then translate back to German and compare.
Find this works pretty well. If this doesn’t work i‘ll get a German to read it. I‘m old-school like that.
Same here, deepl is perfect!