By examining publicly accessible benchmarks, comparable large language models and the latest research papers, we can discern the ways in which GPT4 (integrated into Bing or otherwise) will beat ChatGPT.
I’ll show you how unreleased models already beat current ChatGPT and all of this will actually give a clearer insight into what even GPT5 and rival models from Google might well be able to achieve.
https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html
https://cloud.google.com/blog/topics/tpus/google-showcases-cloud-tpu-v4-pods-for-large-model-training
https://arxiv.org/pdf/2204.02311.pdf
https://arxiv.org/pdf/1905.00537.pdf
https://arxiv.org/pdf/2201.11903.pdf
https://github.com/google/BIG-bench/
https://github.com/google/BIG-bench/blob/main/bigbench/benchmark_tasks/README.md
https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/gre_reading_comprehension
https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/logical_args
https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/evaluating_information_essentiality
https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/physics
http://web.mit.edu/~yczeng/Public/WORKBOOK%201%20FULL.pdf
https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/sufficient_information
https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/implicatures
https://github.com/google/BIG-bench/tree/main/bigbench/benchmark_tasks/winowhy
https://www.deepmind.com/publications/an-empirical-analysis-of-compute-optimal-large-language-model-training
https://lambdalabs.com/blog/nvidia-h100-gpu-deep-learning-performance-analysis#:~:text=Compared%20to%20NVIDIA’s%20previous%2Dgeneration,multiprocessors%2C%20and%20higher%20clock%20frequency.
https://www.patreon.com/AIExplained
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