Generative Artificial Intelligence Reproducibility and Consensus
Published in arXiv, 2023
Recommended citation: Edward Kim, Isamu Isozaki, Naomi Sirkin, and Michael Robson. "Generative Artificial Intelligence Reproducibility and Consensus." arXiv preprint arXiv:2307.01898 (2023). https://arxiv.org/abs/2307.01898
Reproducibility is one of the pillars of scientific research for verifiability, benchmarking, trust, and transparency. We compared artificially generated data samples to answer the critical question - can we reproduce the results of generative AI models? This work provides a practical, solid foundation for AI verification, reproducibility, and consensus for generative AI applications.
Recommended citation: Edward Kim, Isamu Isozaki, Naomi Sirkin, and Michael Robson. “Generative Artificial Intelligence Reproducibility and Consensus.” arXiv preprint arXiv:2307.01898 (2023).