Cloze Style

Cloze tasks, which involve predicting missing words in sentences or passages, are a widely used benchmark for evaluating language model comprehension and knowledge retrieval. Current research focuses on improving the design of cloze prompts to enhance performance, exploring various architectures like bidirectional transformers and multimodal LLMs to handle diverse data types (e.g., comics), and developing methods to control the difficulty of cloze questions for adaptive testing. These advancements are significant for improving the reliability and applicability of language models across various NLP tasks, including question answering, text classification, and recommendation systems.