GPT-4 Performance on Traditional NLP Benchmarks
Across a broad suite of academic natural language processing benchmarks—such as MMLU, HellaSwag, ARC, WinoGrande, HumanEval, and GSM-8K—GPT-4 evaluated few-shot substantially outperforms preceding large language models. Moreover, it surpasses previous state-of-the-art models that relied on benchmark-specific fine-tuning or hand-engineering across nearly all evaluated datasets, with the exception of the DROP reading comprehension and arithmetic benchmark.
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Prep Sessions
Frontier Foundation Models, Capability Evaluation, and Just-In-Time Agent Harnesses @ University of Michigan - Ann Arbor
Ch.1 Foundation Model Capabilities and Benchmarking - Frontier Foundation Models, Capability Evaluation, and Just-In-Time Agent Harnesses @ University of Michigan - Ann Arbor
Academic and Professional Benchmark Performance - Frontier Foundation Models, Capability Evaluation, and Just-In-Time Agent Harnesses @ University of Michigan - Ann Arbor
Learn After
Across nearly all evaluated academic natural language processing datasets, GPT-4 surpassed previous state-of-the-art models that relied on benchmark-specific fine-tuning or hand-engineering.
Identify the benchmark that was the sole exception to GPT-4 surpassing previous state-of-the-art models, and state the two specific capabilities it tests.
Describe how GPT-4 compares to preceding large language models on traditional academic natural language processing benchmarks, and specify the evaluation condition under which this comparison was made.