Factual Consistency

Factual consistency in text generation, particularly in summarization, focuses on ensuring automatically generated text accurately reflects the source material, minimizing hallucinations and inconsistencies. Current research emphasizes developing robust evaluation metrics, often leveraging large language models (LLMs) and techniques like natural language inference and question answering, to identify and quantify factual errors at various granularities (e.g., sentence, entity, or fact level). These advancements are crucial for improving the reliability and trustworthiness of AI-generated content across diverse applications, ranging from clinical notes to news summaries, and for building more responsible and effective natural language processing systems.