---
title: "Shallow Synthesis of Knowledge in GPT-Generated Texts: A"
description: "To score them, the authors propose a citation-graph method. 35 references, cited by 13 papers here."
authors: ["Anna Martin-Boyle; Aahan Tyagi; Marti A. Hearst;", "Dongyeop Kang"]
year: 2024
venue: "arXiv:2402.12255"
source: "https://arxiv.org/abs/2402.12255"
doi: "10.48550/arxiv.2402.12255"
arxiv: "2402.12255"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/martin-boyle-2024-shallow-synthesis-knowledge-gpt-generated/"
---
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# Shallow Synthesis of Knowledge in GPT-Generated Texts: A Case Study in Automatic Related Work Composition

**Authors:** Anna Martin-Boyle; Aahan Tyagi; Marti A. Hearst; and [Dongyeop Kang](https://md.research.elamur.ai/free/en/authors/dongyeop-kang/) · **Year:** 2024 · **Venue:** arXiv:2402.12255 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2402.12255) · [PDF](https://arxiv.org/pdf/2402.12255) · [DOI 10.48550/arxiv.2402.12255](https://doi.org/10.48550/arxiv.2402.12255) · [arXiv 2402.12255](https://arxiv.org/abs/2402.12255) · [OpenAlex](https://openalex.org/W4391988175)

**Citations:** 3 citations (OpenAlex, 2026-10-14) · [13](https://md.research.elamur.ai/free/en/papers/martin-boyle-2024-shallow-synthesis-knowledge-gpt-generated/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The authors ask whether GPT-4 can genuinely synthesize prior literature when writing Related Work sections, or whether it only lists sources. They built ScholaCite, a web tool in which GPT-4 proposes groupings of cited papers with rationales and then drafts text that a user can edit. The test bed is ten best papers from ACL 2023. For each paper, the model receives a "work-in-progress" version with the original Related Work section removed, plus the abstracts of the works that section cited.

Three versions are compared: the original human text, a human-AI collaborative (GPT-assisted) text, and a fully GPT-generated one. To score them, the authors propose a citation-graph method. Each cited work is a node, and an edge links two works cited in the same sentence. Graph structure then serves as a reproducible proxy for how well a text connects its sources.

Purely generated sections produce sparse, weakly linked graphs. Collaborative ones look structurally close to human writing. The authors conclude that LLMs can help brainstorm citation groupings but should not draft sections on their own.

## Key points
- **Graph density:** human-written 0.14 and GPT-assisted 0.13, versus only 0.02 for GPT-generated texts.
- **Average clustering coefficient:** human-written 0.66 and GPT-assisted 0.63, versus 0.15 for GPT-generated texts. Human and collaborative texts arrange citations into clearer subgroups.
- **Inputs:** the model saw only abstract-level information about cited works. The authors name this and the limited context window as likely reasons for shallow synthesis.
- **Limitations:**
  - The sample is small (10 NLP papers).
  - Only one prompt and hyperparameter setup was tested.
  - ScholaCite was used by just one of the authors, so no user study was done.
- **Design recommendation:** writing assistants should support repetitive tasks, such as organizing citations, rather than generating full sections of prose.

*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

- [University of California, Berkeley](https://md.research.elamur.ai/free/en/universities/university-of-california-berkeley/) — University, Berkeley, United States · 175 works · 227,440 citations
- [University of Minnesota](https://md.research.elamur.ai/free/en/universities/university-of-minnesota/) — University, Minneapolis, United States · 28 works · 2,184 citations

📚 Shelves (6): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗄️ Information Systems](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · 1,437 works · [🎯 Decision Sciences](https://md.research.elamur.ai/free/en/shelves/decision-sciences/) · 209 works · [🎯 Statistics, Probability and Uncertainty](https://md.research.elamur.ai/free/en/shelves/decision-sciences/statistics-probability-and-uncertainty/) · 61 works · [🎭 History and Philosophy of Science](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/history-and-philosophy-of-science/) · 53 works

🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,320 works

## Cite

Cite as: Anna Martin-Boyle; Aahan Tyagi; Marti A. Hearst; and Dongyeop Kang. 2024. Shallow Synthesis of Knowledge in GPT-Generated Texts: A Case Study in Automatic Related Work Composition. arXiv:2402.12255.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{hearst2024shallow,
  title = {{Shallow Synthesis of Knowledge in GPT-Generated Texts: A Case Study in Automatic Related Work Composition}},
  author = {Anna Martin-Boyle; Aahan Tyagi; Marti A. Hearst; and Dongyeop Kang},
  year = {2024},
  doi = {10.48550/arxiv.2402.12255},
  eprint = {2402.12255},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2402.12255}
}
```

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## References

35 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 31 October 2026*