2025/08/21 by Tobias Schreieder, Tim Schopf, Schreieder, Tobias +3 · 4 citations
Computer Science · Medicine · Social Sciences · #Artificial Intelligence in Healthcare and Education #Bridge (graph theory) #Computational and Text Analysis Methods #Field (mathematics) #Focus (optics) #Key (lock) #Language model #Taxonomy (biology) #Topic Modeling #Traceability
paper · pdf · doi:10.18653/v1/2026.acl-long.1430
openalex created_date 2025/10/10 · openalex publication_date 2026/01/01 · openalex updated_date 2026/08/02
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.