If they ever get around to changing fundamentally how it works, I’ll give it another try. I don’t like having to second guess everything it says, defeats the entire purpose of trying to use it If I have to verify everything. We made a similar but pared down output generator in college once, that worked in essentially the same way. We fed it a bunch of Freida Khala poems so all it would output was strange fragments of those. But that experience taught me, perhaps too well, how these things work under the hood. I can’t imagine how many guardrails and parameters they have set up just to get it functional.
I doubt OpenAI will fundamentally change how GPT works, but you never know. Possibly the next gen of LLM’s will learn from the failings of this generation and be fundamentally constructed better.
Not saying I don’t realise the cost, either. Better LLM’s will arguably have a much worse outcome for humanity. I’m just talking here about personal ways to get some use out of them. I actually started writing an article about how this might help in brainstorming sketches, but I’m probably not going to ever finish / publish it.
For applications like language translation they’re the best automated tools we have. But its too little to justify how over extended the investment into it is.
Yes. An LLM is just a model trained on a large corpus of text, which DeepL absolutely is. It uses a transformer architecture – which literally could not work without it.
Thanks. And it’s indeed very good from my experience.
Google Translate has the pronunciation database (a separate project) and the ability to translate from whole images, but it’s pretty clearly not as good.
If they ever get around to changing fundamentally how it works, I’ll give it another try. I don’t like having to second guess everything it says, defeats the entire purpose of trying to use it If I have to verify everything. We made a similar but pared down output generator in college once, that worked in essentially the same way. We fed it a bunch of Freida Khala poems so all it would output was strange fragments of those. But that experience taught me, perhaps too well, how these things work under the hood. I can’t imagine how many guardrails and parameters they have set up just to get it functional.
I doubt OpenAI will fundamentally change how GPT works, but you never know. Possibly the next gen of LLM’s will learn from the failings of this generation and be fundamentally constructed better.
Not saying I don’t realise the cost, either. Better LLM’s will arguably have a much worse outcome for humanity. I’m just talking here about personal ways to get some use out of them. I actually started writing an article about how this might help in brainstorming sketches, but I’m probably not going to ever finish / publish it.
For applications like language translation they’re the best automated tools we have. But its too little to justify how over extended the investment into it is.
Would DeepL actually be considered an LLM?
Yes. An LLM is just a model trained on a large corpus of text, which DeepL absolutely is. It uses a transformer architecture – which literally could not work without it.
Thanks. And it’s indeed very good from my experience.
Google Translate has the pronunciation database (a separate project) and the ability to translate from whole images, but it’s pretty clearly not as good.
The wikipedia page says that exactly how they work is proprietary, so who really knows.