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

Paper · 2026 · Tobias Schreieder, Tim Schopf and Michael Färber

Summary

Work on making LLM outputs traceable to supporting sources is growing quickly but is scattered. Different groups describe similar goals as citation, attribution or quotation, and they evaluate their systems in incompatible ways. The authors ran a PRISMA-based systematic mapping study. From 805 deduplicated papers, manual screening kept 134 relevant studies. They grouped these under one paradigm, which they call evidence-based text generation with LLMs.

From this corpus they built a faceted taxonomy with three independent axes:

  • Attribution approach: parametric or non-parametric, each split into finer classes.
  • Citation characteristics: modality, evidence level, style, visibility and frequency.
  • Task.

They also catalogued 300 evaluation metrics, sorted by seven dimensions and six methods, plus 19 frameworks, 231 datasets and 11 benchmarks. The annotated collection is released publicly.

The review finds that the field depends mostly on retrieval before generation. Attribution from model-internal knowledge is rarely studied, and evidence is almost always text. Evaluation tools are seldom reused across studies. These findings give researchers a common vocabulary and identify gaps worth addressing, such as standardized evaluation, explainable citation choices and multimodal evidence.

Key points

  • Of the 134 studies, 75% aim to cite, 62% to attribute and 13% to quote; some papers use more than one framing. Output rose from 28 papers in 2023 to 96 in 2024, and over 75% of the corpus appeared after 2023.
  • The authors count 126 non-parametric and 25 parametric attribution approaches.
    • Non-parametric approaches are split into post-retrieval (the most common, including RAG with references), post-generation, in-generation and in-context.
    • Parametric approaches are split into pure LLM, model-centric and data-centric; 72% of parametric papers only evaluate pure LLMs.
  • Text is the citation modality in 96% of studies. Tables, graphs and visual evidence such as images and bounding boxes are rarely used.
  • Across the 300 catalogued metrics, reuse is limited: just two of the frameworks and two of the benchmarks show up regularly across studies, and human evaluation protocols differ considerably from one study to another.
  • The search was run in February 2025 across nine databases and limited to English-language work. A sensitivity check with extra search terms found only about 4% additional relevant papers. The annotated dataset is at github.com/faerber-lab/AttributeCiteQuote.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🔬National Institute of InformaticsTokyo · Japan38 works · 3,247 citations🎓Technische Universität DresdenDresden · Germany13 works · 7,336 citations🎓Center for Scalable Data Analytics and Artificial IntelligenceDresden · Germany10 works · 77 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 works

🧭 Topics 1Research subjects and languages the work is about.Text generation · 214 works

Cite

Cite as Tobias Schreieder, Tim Schopf and Michael Färber. 2026. Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Models. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1430

@inproceedings{schreieder2026attribution,
  title = {{Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Models}},
  author = {Tobias Schreieder, Tim Schopf and Michael Färber},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1430},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1430}
}

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