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Found 3 papers, 1 papers with code
Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning
Generative Commonsense Reasoning (GCR) requires a model to reason about a situation using commonsense knowledge, while generating coherent sentences.
Enhancing Texture Generation with High-Fidelity Using Advanced Texture Priors
Moreover, background noise frequently arises in high-resolution texture synthesis, limiting the practical application of these generation technologies. In this work, we propose a high-resolution and high-fidelity texture restoration technique that uses the rough texture as the initial input to enhance the consistency between the synthetic texture and the initial texture, thereby overcoming the issues of aliasing and blurring caused by the user's structure simplification operations.
Learning to Predict Concept Ordering for Common Sense Generation
Prior work has shown that the ordering in which concepts are shown to a commonsense generator plays an important role, affecting the quality of the generated sentence.
