341–350 of 723 posts
Another naming disaster! Opus is better than sonnet? And sonnet is better than haiku? Perhaps this makes sense to people familiar with sonnets and haikus and opus....es? Nonsensical to me! I know everyone loves to hate on Google, but at least pro and ultra have a sort of sense of level of sophistication.
I think the intention was more "bigger" than better - but opus is an odd choice. haiku>sonnet>ballad maybe? haiku>sonnet>epic?
dang; missed opportunity.
The APPS benchmark result of Claude 3 Opus at 70.2% indicates it might be quite useful for coding. The dataset measures the ability to convert problem descriptions to Python code. The average length of a problem is nearly 300 words. Interestingly, no other top models have published results on this benchmark. Claude 3 Model Card: https://www-cdn.anthropic.com/de8ba9b01c9ab7cbabf5c33b80b7bb... Table 1: Evaluation resul…
(full disclosure, I work at Anthropic) Opus has definitely been writing a lot of my code at work recently :)
I don't put a lot of stock on evals. many of the models claiming gpt-4 like benchmark scores feel a lot worse for any of my use-cases. Anyone got any sample output? Claude isn't available in EU yet, else i'd try it myself. :(
The one for chat: https://www.anthropic.com/claude-ai-locations
The one for API: https://www.anthropic.com/supported-countries
The latter has Norway in it, while the former does not. One wonders why.
The APPS benchmark result of Claude 3 Opus at 70.2% indicates it might be quite useful for coding. The dataset measures the ability to convert problem descriptions to Python code. The average length of a problem is nearly 300 words. Interestingly, no other top models have published results on this benchmark. Claude 3 Model Card: https://www-cdn.anthropic.com/de8ba9b01c9ab7cbabf5c33b80b7bb... Table 1: Evaluation resul…
“Claude 3 gets ~60% accuracy on GPQA. It's hard for me to understate how hard these questions are—literal PhDs (in different domains from the questions) [spending over 30 minutes] with access to the internet get 34%. PhDs in the same domain (also with internet access!) get 65% - 75% accuracy.” — David Rein, first author of the GPQA Benchmark. I added text in […] based on the benchmark paper’s abstract. https://twitte…
Earlier quoted context omitted.
FWIW the use case you're describing is very often achievable with RAG. Embedding models are deterministic, so while you're still limited by the often-nondeterministic nature of the LLM, in practice you can usually get the same answer for the same input. And it's substantially cheaper to do.
With 1M tokens, if snapshotting the LLM state is cheap, it would beat out-of-the-box nearly all RAG setups, except the ones dealing with large datasets. 1M tokens is a lot of docs.
The APPS benchmark result of Claude 3 Opus at 70.2% indicates it might be quite useful for coding. The dataset measures the ability to convert problem descriptions to Python code. The average length of a problem is nearly 300 words. Interestingly, no other top models have published results on this benchmark. Claude 3 Model Card: https://www-cdn.anthropic.com/de8ba9b01c9ab7cbabf5c33b80b7bb... Table 1: Evaluation resul…
“Claude 3 gets ~60% accuracy on GPQA. It's hard for me to understate how hard these questions are—literal PhDs (in different domains from the questions) [spending over 30 minutes] with access to the internet get 34%. PhDs in the same domain (also with internet access!) get 65% - 75% accuracy.” — David Rein, first author of the GPQA Benchmark. I added text in […] based on the benchmark paper’s abstract. https://twitte…
Want to know how well the new Google model performs compared to GPT-4? Look at the Claude benchmark table.