Unlocking Neural Text Generation’s Full Potential: A Comparative Study

In this article, we explore the concept of "degeneration" in machine learning-based text generation models, particularly those used for poetry generation. Degeneration refers to the tendency of these models to produce repetitive, bland, or incoherent text when using maximization-based decoding strategies such as beam search. To address this issue, we propose two sampling techniques: Top-K and Nucleus sampling.
Top-K sampling limits the possible words to the top 20, while Nucleus sampling expands and contracts the candidate pool dynamically with a threshold of 0.9 for expanding and contracting the pool. We also use a scaling factor called temperature to shape the distribution before sampling. By using these techniques, we can generate more diverse and coherent poems that are less likely to get stuck in repetitive loops or produce bland text.
To simplify the concept of degeneration, think of it like a rollercoaster ride. When a machine learning model is trained on a dataset, it’s like riding a rollercoaster through a beautiful and diverse landscape. However, when using maximization-based decoding strategies to generate text, the model can get stuck in repetitive loops or produce bland text, much like how a rollercoaster can get stuck on a loop. By using sampling techniques like Top-K and Nucleus sampling, we can create a more diverse and coherent landscape of words, like a rollercoaster that takes riders through a thrilling and exciting journey.
In conclusion, degeneration is a common issue in machine learning-based text generation models, particularly those used for poetry generation. By using sampling techniques like Top-K and Nucleus sampling, we can generate more diverse and coherent poems that are less likely to get stuck in repetitive loops or produce bland text. These techniques help create a more thrilling and exciting journey through the landscape of words, much like how they can help create a more diverse and enjoyable rollercoaster ride.