Performance of Generative Large Language Models on Ophthalmology Board-Style Questions

Summary: Natural Language Generation (NLG) has made significant progress in recent years, thanks to deep learning technologies like Transformer-based language models. This advancement has resulted in more fluent and coherent NLG, leading to improvements in tasks such as summarization, dialogue generation, and data-to-text conversion. However, deep learning-based generation is prone to producing unintended text, which affects system performance and fails to meet user expectations in real-world scenarios. To address this issue, researchers have conducted studies on measuring and mitigating hallucinated texts. This survey provides an overview of the research progress and challenges in the hallucination problem in NLG, covering metrics, mitigation methods, and task-specific research progress.

Summary: Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data, achieving significant advancements and performance in various applications. This paper aims to summarize different variants of GANs and their potential applications in different domains.