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GPT-3: Language Models are Few-Shot Learners - GitHub GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic
gpt-engineer - GitHub gpt-engineer installs the binary 'bench', which gives you a simple interface for benchmarking your own agent implementations against popular public datasets The easiest way to get started with benchmarking is by checking out the template repo, which contains detailed instructions and an agent template
GitHub - openai gpt-2: Code for the paper Language Models are . . . The dataset our GPT-2 models were trained on contains many texts with biases and factual inaccuracies, and thus GPT-2 models are likely to be biased and inaccurate as well To avoid having samples mistaken as human-written, we recommend clearly labeling samples as synthetic before wide dissemination