PoemTale Diffusion generates a coherent set of images from a poem by combining emotion-based segmentation, multi-stage LLM prompt refinement, and consistent self-attention, outperforming direct poem-to-image approaches on CLIP and human metrics.
GPoeT-2: A GPT-2 Based Poem Generator
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abstract
This project aims to produce the next volume of machine-generated poetry, a complex art form that can be structured and unstructured, and carries depth in the meaning between the lines. GPoeT-2 is based on fine-tuning a state of the art natural language model (i.e. GPT-2) to generate limericks, typically humorous structured poems consisting of five lines with a AABBA rhyming scheme. With a two-stage generation system utilizing both forward and reverse language modeling, GPoeT-2 is capable of freely generating limericks in diverse topics while following the rhyming structure without any seed phrase or a posteriori constraints.Based on the automated generation process, we explore a wide variety of evaluation metrics to quantify "good poetry," including syntactical correctness, lexical diversity, and subject continuity. Finally, we present a collection of 94 categorized limericks that rank highly on the explored "good poetry" metrics to provoke human creativity.
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PoemTale Diffusion: Minimising Information Loss in Poem to Image Generation with Multi-Stage Prompt Refinement
PoemTale Diffusion generates a coherent set of images from a poem by combining emotion-based segmentation, multi-stage LLM prompt refinement, and consistent self-attention, outperforming direct poem-to-image approaches on CLIP and human metrics.