Model Criticism for Long-Form Text Generation
Yuntian Deng, Volodymyr Kuleshov, Alexander M. Rush · 2022
Language models have demonstrated the ability to generate highly fluent text; however, it remains unclear whether their output retains coherent high-level structure (e.g., story progression).Here, we propose to apply a statistical tool, model criticism in latent space, to evaluate the high-level structure of the generated text.Model criticism compares the distributions between real and generated data in a latent space obtained according to an assumptive generative process.Different generative processes identify specific failure modes of the underlying model.We perform experiments on three representative aspects of highlevel discourse-coherence, coreference, and topicality-and find that transformer-based language models are able to capture topical structures but have a harder time maintaining structural coherence or modeling coreference.