What approaches support AI-generated poetry with thematic coherence and emotional resonance

Question

Can you name approaches to support ai-generated poetry with thematic coherence and emotional resonance?

Answer 1

In order to support AI-generated poetry with thematic coherence and emotional resonance, you can refer to the strategies below:

• Fine-tuning on Poetry Data: Train the model on diverse poetry datasets to learn poetic structure and themes.
• Emotion-Control Tokens: Incorporate special tokens to guide the emotional tone (e.g., "<joy>", "<melancholy>").
• Prompt Engineering: Use detailed prompts specifying themes, styles, or emotions.
• Beam Search or Top-p Sampling: Optimize decoding strategies for coherent and creative outputs.

Here is the code snippet you can refer to:

In the above code, we are using Fine-Tuning to enhance structure and thematic depth, Control Tokens to guide emotional and stylistic coherence, and Prompt Design to tailor outputs to desired themes and moods.

Hence, using these strategies, you can support AI-generated poetry with thematic coherence and emotional resonance.