---
title: "Attribute First, then Generate — paper by Slobodkin et al. (2024)"
description: "The authors define a stricter task, Locally-attributable Grounded Text Generation. 56 references, cited by 33 papers here."
authors: ["Aviv Slobodkin", "Eran Hirsch", "Arie Cattan", "Tal Schuster", "Ido Dagan"]
year: 2024
venue: "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2024.acl-long.182/"
doi: "10.18653/v1/2024.acl-long.182"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/slobodkin-2024-attribute-first-then-generate-locally/"
---
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# Attribute First, then Generate: Locally-attributable Grounded Text Generation

**Authors:** [Aviv Slobodkin](https://md.research.elamur.ai/free/en/authors/aviv-slobodkin/), [Eran Hirsch](https://md.research.elamur.ai/free/en/authors/eran-hirsch/), [Arie Cattan](https://md.research.elamur.ai/free/en/authors/arie-cattan/), [Tal Schuster](https://md.research.elamur.ai/free/en/authors/tal-schuster/) and [Ido Dagan](https://md.research.elamur.ai/free/en/authors/ido-dagan/) · **Year:** 2024 · **Venue:** [Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2024](https://md.research.elamur.ai/free/en/venues/acl/2024/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.acl-long.182/) · [PDF](https://aclanthology.org/2024.acl-long.182.pdf) · [DOI 10.18653/v1/2024.acl-long.182](https://doi.org/10.18653/v1/2024.acl-long.182) · [OpenAlex](https://openalex.org/W4402684029)

**Citations:** 15 citations (OpenAlex, 2026-10-14) · [33](https://md.research.elamur.ai/free/en/papers/slobodkin-2024-attribute-first-then-generate-locally/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Attributed generation tries to curb LLM hallucinations by attaching citations to outputs. However, existing systems usually cite whole documents or paragraphs, so checking a claim still means reading a lot of irrelevant text. The authors define a stricter task, Locally-attributable Grounded Text Generation. In it, each output sentence must be fully supported by citations to minimal, possibly non-contiguous source spans. Their method, "Attribute First, then Generate," reverses the usual order and runs in three stages. First, it selects relevant spans, guided by salience or query relevance depending on the task. Second, it groups those spans into ordered clusters, one per sentence. Third, it writes the text one sentence at a time, conditioning each sentence on its cluster. Because the selected spans drive generation, they also serve as the citations. The approach was tested on Multi-document Summarization and Long-form Question-answering, with both few-shot prompting and fine-tuned components. It matched or beat baselines on output quality and kept attribution accuracy high. Its citations were far shorter, and human fact-checkers verified outputs in roughly half the time. This makes grounded generation easier to verify in practice.

## Key points
- The pipeline has three steps: content selection, then sentence-level planning (clustering spans), then sentence-by-sentence generation that conditions on prior sentences and the next cluster.
- Evaluation data include DUC, TAC, Multi-News, and annotation data from Liu et al. (2023). Human evaluation was run on Amazon Mechanical Turk.
- Citations came out orders of magnitude shorter than document-level baselines. Human verification time dropped by about 50%, with little loss in attribution accuracy.
- Limitations: multiple model calls make the method slower and more compute-hungry than end-to-end generation, and errors can propagate between pipeline stages.
- Code is available at github.com/lovodkin93/attribute-first-then-generate.

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

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## Cite

Cite as: Aviv Slobodkin, Eran Hirsch, Arie Cattan, Tal Schuster and Ido Dagan. 2024. Attribute First, then Generate: Locally-attributable Grounded Text Generation. Proceedings of the 62nd 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.

```bibtex
@inproceedings{slobodkin2024attribute,
  title = {{Attribute First, then Generate: Locally-attributable Grounded Text Generation}},
  author = {Aviv Slobodkin, Eran Hirsch, Arie Cattan, Tal Schuster and Ido Dagan},
  booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2024},
  doi = {10.18653/v1/2024.acl-long.182},
  url = {https://doi.org/10.18653/v1/2024.acl-long.182}
}
```

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*Updated 30 October 2026*