Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Models

The increasing adoption of large language models (LLMs) has raised serious concerns about their reliability and trustworthiness.As a result, a growing body of research focuses on evidence-based text generation with LLMs, aiming to link model outputs to supporting evidence to ensure traceability and verifiability.However, the field is fragmented due to inconsistent terminology, isolated evaluation practices, and a lack of unified benchmarks.To bridge this gap, we systematically analyze 134 papers, introduce a unified taxonomy of evidence-based text generation with LLMs, and investigate 300 evaluation metrics across seven key dimensions.Thereby, we focus on approaches that use citations, attribution, or quotations for evidence-based text generation.Building on this, we examine the distinctive characteristics and representative methods in the field.Finally, we highlight open challenges and outline promising directions for future work.