Yes Yes

Research on "Yes/No" related tasks focuses on improving the accuracy and reliability of models processing natural language and multimodal data involving affirmation and negation. Current efforts concentrate on developing robust models for tasks such as question answering, satire detection, and code suggestion, often employing techniques like curriculum learning, pinpoint tuning, and uncertainty quantification to address challenges like sycophancy in LLMs and the inherent ambiguity in human language. These advancements are significant for improving the reliability and trustworthiness of AI systems across various applications, from autonomous driving to open-domain question answering.