Bits of Grass: Does GPT already know how to write like Whitman?
This study examines the ability of GPT-3.5, GPT-3.5-turbo (ChatGPT) and GPT-4 models to generate poems in the style of specific authors using zero-shot and many-shot prompts (which use the maximum context length of 8192 tokens). We assess the performance of models that are not fine-tuned for generating poetry in the style of specific authors, via automated evaluation. Our findings indicate that without fine-tuning, even when provided with the maximum number of 17 poem examples (8192 tokens) in the prompt, these models do not generate poetry in the desired style.
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Methods
Absolute Position Encodings •
Adam •
Attention Dropout •
BPE •
Cosine Annealing •
Dense Connections •
Dropout •
Fixed Factorized Attention •
GELU •
GPT-3 •
GPT-4 •
Label Smoothing •
Layer Normalization •
Linear Layer •
Linear Warmup With Cosine Annealing •
Multi-Head Attention •
Position-Wise Feed-Forward Layer •
Residual Connection •
Scaled Dot-Product Attention •
Softmax •
Strided Attention •
Transformer •
Weight Decay
