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LLMs are extremely good at translation, given that the transformer was literally built for that.
Maybe in some cases. But generally speaking the consensus in the language translation industry is that NMT (e.g. Google Translate) still provides higher quality than current gen LLMs.
Earlier quoted context omitted.
LLMs are extremely good at translation, given that the transformer was literally built for that.
Maybe in some cases. But generally speaking the consensus in the language translation industry is that NMT (e.g. Google Translate) still provides higher quality than current gen LLMs.
(Also LLMs are wonderful at solving the "tip of my tongue" problem - "what's the English word for $this doing $that, kind of like $example1 but without $aspect1?...")
Notably, this prompt is making "hallucinations" an officially recognized phenomenon: > If Claude is asked about a very obscure person, object, or topic, i.e. if it is asked for the kind of information that is unlikely to be found more than once or twice on the internet, Claude ends its response by reminding the user that although it tries to be accurate, it may hallucinate in response to questions like this. It uses…
How can Claude "know" whether something "is unlikely to be found more than once or twice on then internet"? Unless there are other sources that explicitly say "[that thing] is obscure". I don't think LLMs can report if something was encountered more/less often in their training data, there are too many weights and neither us nor them know exactly what each of them represents.
https://claude.site/artifacts/605e9525-630e-4782-a178-020e15...
It is funny, because it says things like “yak milk cheese making tutorials” and “ancient Sumerian pottery catalogs”. But that’s only the extremely rare. The things for “only once or twice” are “the location of jimmy Hoffa’s remains” and “banksy’s true identity.”
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The limitations on processing letters aren’t in the math, they are in the encoding. Language is the map, and concepts are the territory. You may as well complain that someone doesn’t really understand their neighborhood if they can’t find it on a map.
>they are in the encoding Is encoding not math?
It's not like humans read letter by letter either, at least not past the age of 6 or such. They can, if needed, but it requires extra effort. Same is true with LLMs.
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Maybe in some cases. But generally speaking the consensus in the language translation industry is that NMT (e.g. Google Translate) still provides higher quality than current gen LLMs.
Is it? Anecdotally, even GPT 3.5 felt better than Google Translate; where we are with GPT-4o, it feels both better in terms of quality, accuracy and context awareness and it has a much better UI/UX. Like, I can't tell Google Translate it picked the wrong translation for a specific phrase (I thing I could in the past, but this feature seems missing), or otherwise inform it of extra context. (Also LLMs are wonderful at…
It’s also possible that the cost of LLMs outweigh their benefit for this specific use case.
The only vendor I know of doing LLM translation in production is DeepL, and only supports 3 languages, launched last week.
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It probably shouldn't be called prompt engineering , even informally. The work of an engineer shouldn't require hope .
This is the fundamental change in the concept of programming From computer’s doing exactly what you state, with all the many challenges that creates To is probabilistically solving for your intent, with all the many challenges that creates Fair to say human beings probably need both to effectively communicate Will be interesting to see if the current GenAI + ML + prompt engineering + code is sufficient
I can absolutely put into words what I want, but I cannot program it because of all the variables. When a computer can build the code for me based on my description... Holy cow.
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It probably shouldn't be called prompt engineering , even informally. The work of an engineer shouldn't require hope .
It should be called prompt science.
I never thought my English degree would be so useful.
This is only half in jest by the way.
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This is the fundamental change in the concept of programming From computer’s doing exactly what you state, with all the many challenges that creates To is probabilistically solving for your intent, with all the many challenges that creates Fair to say human beings probably need both to effectively communicate Will be interesting to see if the current GenAI + ML + prompt engineering + code is sufficient
Honestly, this sort of programming (whether it's in quotes or not) will be unbelievably life changing when it works. I can absolutely put into words what I want, but I cannot program it because of all the variables. When a computer can build the code for me based on my description... Holy cow.
> Why? This seems really dumb.