arXiv:2007.06290
공식 코드 · Papers with Code
Paranoid Transformer: Reading Narrative of Madness as Computational Approach to Creativity
원문 초록
This papers revisits the receptive theory in context of computational creativity. It presents a case study of a Paranoid Transformer - a fully autonomous text generation engine with raw output that could be read as the narrative of a mad digital persona without any additional human post-filtering. We describe technical details of the generative system, provide examples of output and discuss the impact of receptive theory, chance discovery and simulation of fringe mental state on the understanding of computational creativity.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Making Language Model a Hierarchical Classifier and Generator
Mitigating Object Hallucinations via Sentence-Level Early Intervention
The Devil behind the mask: An emergent safety vulnerability of Diffusion LLMs
Hashed Watermark as a Filter: Defeating Forging and Overwriting Attacks in Weight-based Neural Network Watermarking
Seq vs Seq: An Open Suite of Paired Encoders and Decoders
OpenFActScore: Open-Source Atomic Evaluation of Factuality in Text Generation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.