2025/08/21 by Tobias Schreieder, Tim Schopf, Schreieder, Tobias +3 · 3 citations
Computer Science · Social Sciences · Medicine · #Topic Modeling #Computational and Text Analysis Methods #Artificial Intelligence in Healthcare and Education
paper · pdf · doi:10.18653/v1/2026.acl-long.1430
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.