The Nonlinear Library AF - Anthropic release Claude 3, claims >GPT-4 Performance by Lawrence Chan
Mar 4, 2024
02:26
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Anthropic release Claude 3, claims >GPT-4 Performance, published by Lawrence Chan on March 4, 2024 on The AI Alignment Forum.
Today, we're announcing the Claude 3 model family, which sets new industry benchmarks across a wide range of cognitive tasks. The family includes three state-of-the-art models in ascending order of capability: Claude 3 Haiku, Claude 3 Sonnet, and Claude 3 Opus. Each successive model offers increasingly powerful performance, allowing users to select the optimal balance of intelligence, speed, and cost for their specific application.
Better performance than GPT-4 on many benchmarks
The largest Claude 3 model seems to outperform GPT-4 on benchmarks (though note slight differences in evaluation methods):
Opus, our most intelligent model, outperforms its peers on most of the common evaluation benchmarks for AI systems, including undergraduate level expert knowledge (MMLU), graduate level expert reasoning (GPQA), basic mathematics (GSM8K), and more. It exhibits near-human levels of comprehension and fluency on complex tasks, leading the frontier of general intelligence.
I haven't had the chance to interact much with this model yet, but as of writing, Manifold assigns ~70% probability to Claude 3 outperforming GPT-4 on the LMSYS Chatbot Arena Leaderboard.
https://manifold.markets/JonasVollmer/will-claude-3-outrank-gpt4-on-the-l?r=Sm9uYXNWb2xsbWVy
Synthetic data?
According to Anthropic, Claude 3 was trained on synthetic data (though it was not trained on any customer-generated data from previous models):
Also interesting that the model can identify the synthetic nature of some of its evaluation tasks. For example, it provides the following response to a synthetic recall text:
Will Anthropic push the frontier of AI development?
Several people have pointed out that this post seems to take a different stance on race dynamics than was expressed previously:
As we push the boundaries of AI capabilities, we're equally committed to ensuring that our safety guardrails keep apace with these leaps in performance. Our hypothesis is that being at the frontier of AI development is the most effective way to steer its trajectory towards positive societal outcomes.
Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.
Today, we're announcing the Claude 3 model family, which sets new industry benchmarks across a wide range of cognitive tasks. The family includes three state-of-the-art models in ascending order of capability: Claude 3 Haiku, Claude 3 Sonnet, and Claude 3 Opus. Each successive model offers increasingly powerful performance, allowing users to select the optimal balance of intelligence, speed, and cost for their specific application.
Better performance than GPT-4 on many benchmarks
The largest Claude 3 model seems to outperform GPT-4 on benchmarks (though note slight differences in evaluation methods):
Opus, our most intelligent model, outperforms its peers on most of the common evaluation benchmarks for AI systems, including undergraduate level expert knowledge (MMLU), graduate level expert reasoning (GPQA), basic mathematics (GSM8K), and more. It exhibits near-human levels of comprehension and fluency on complex tasks, leading the frontier of general intelligence.
I haven't had the chance to interact much with this model yet, but as of writing, Manifold assigns ~70% probability to Claude 3 outperforming GPT-4 on the LMSYS Chatbot Arena Leaderboard.
https://manifold.markets/JonasVollmer/will-claude-3-outrank-gpt4-on-the-l?r=Sm9uYXNWb2xsbWVy
Synthetic data?
According to Anthropic, Claude 3 was trained on synthetic data (though it was not trained on any customer-generated data from previous models):
Also interesting that the model can identify the synthetic nature of some of its evaluation tasks. For example, it provides the following response to a synthetic recall text:
Will Anthropic push the frontier of AI development?
Several people have pointed out that this post seems to take a different stance on race dynamics than was expressed previously:
As we push the boundaries of AI capabilities, we're equally committed to ensuring that our safety guardrails keep apace with these leaps in performance. Our hypothesis is that being at the frontier of AI development is the most effective way to steer its trajectory towards positive societal outcomes.
Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.