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Text-to-image models have recently achieved remarkable success with seeminglyaccurate samples in photo-realistic quality. However as state-of-the-artlanguage models still struggle evaluating precise statements consistently, sodo language model based image generation processes. In this work we showcaseproblems of state-of-the-art text-to-image models like DALL-E with generatingaccurate samples from statements related to the draw bench benchmark.Furthermore we show that CLIP is not able to rerank those generated samplesconsistently. To this end we propose LogicRank, a neuro-symbolic reasoningframework that can result in a more accurate ranking-system for suchprecision-demanding settings. LogicRank integrates smoothly into the generationprocess of text-to-image models and moreover can be used to further fine-tunetowards a more logical precise model.

LogicRank: Logic Induced Reranking for Generative Text-to-Image Systems