The paper "The Curious Case of Neural Text DeGeneration" by Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi explores the challenges of text generation from neural language models. Despite significant advancements in language modeling, the authors observe that maximizing likelihood, a common decoding strategy, often leads to degenerate output, such as bland, incoherent, or repetitive text. To address this issue, they propose Nucleus Sampling, a method that truncates the unreliable tail of the probability distribution and samples from the dynamic nucleus of tokens containing the majority of the probability mass. This approach avoids the issues of text degeneration and produces higher-quality, more diverse text compared to other decoding strategies like beam search and top-$k$ sampling. The authors compare various distributional properties of generated text, including likelihood, perplexity, and vocabulary usage, and conclude that Nucleus Sampling is the best overall decoding strategy, as demonstrated through human and statistical evaluations. The paper also discusses the limitations of maximization-based decoding and the challenges of open-ended text generation, providing insights into the intrinsic properties of human language and the need for dynamic sampling strategies.The paper "The Curious Case of Neural Text DeGeneration" by Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi explores the challenges of text generation from neural language models. Despite significant advancements in language modeling, the authors observe that maximizing likelihood, a common decoding strategy, often leads to degenerate output, such as bland, incoherent, or repetitive text. To address this issue, they propose Nucleus Sampling, a method that truncates the unreliable tail of the probability distribution and samples from the dynamic nucleus of tokens containing the majority of the probability mass. This approach avoids the issues of text degeneration and produces higher-quality, more diverse text compared to other decoding strategies like beam search and top-$k$ sampling. The authors compare various distributional properties of generated text, including likelihood, perplexity, and vocabulary usage, and conclude that Nucleus Sampling is the best overall decoding strategy, as demonstrated through human and statistical evaluations. The paper also discusses the limitations of maximization-based decoding and the challenges of open-ended text generation, providing insights into the intrinsic properties of human language and the need for dynamic sampling strategies.