# Citing Less Critically: LLMs Reshape the Rhetoric and Reach of Scientific Citation

arXiv: https://arxiv.org/abs/2609.01432 · HTML: https://mlsanity.com/paper/2609.01432/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 1 Sep 2026.

## Builds on (most cited first, 15 of 15)

- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [Structural Scaffolds for Citation Intent Classification in Scientific Publications](https://mlsanity.com/paper/1904.01608/) (2019)
- [CiteME: Can Language Models Accurately Cite Scientific Claims?](https://mlsanity.com/paper/2407.12861/) (2024)
- [AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discovery](https://mlsanity.com/paper/2604.05550/) (2026)
- [Large Language Models Reflect Human Citation Patterns with a Heightened Citation Bias](https://mlsanity.com/paper/2405.15739/) (2024)
- [HLM-Cite: Hybrid Language Model Workflow for Text-based Scientific Citation Prediction](https://mlsanity.com/paper/2410.09112/) (2024)
- [LLM hallucinations in the wild: Large-scale evidence from non-existent citations](https://mlsanity.com/paper/2605.07723/) (2026)
- [Who Gets Cited? Gender- and Majority-Bias in LLM-Driven Reference Selection](https://mlsanity.com/paper/2508.02740/) (2025)
- [LiveBrowseComp: Are Search Agents Searching, or Just Verifying What They Already Know?](https://mlsanity.com/paper/2605.28721/) (2026)
- [Investigating Disagreement in the Scientific Literature](https://mlsanity.com/paper/2107.14641/) (2021)
- [Hallucinations in Bibliographic Recommendation: Citation Frequency as a Proxy for Training Data Redundancy](https://mlsanity.com/paper/2510.25378/) (2025)
- [Citations: Indicators of Quality? The Impact Fallacy](https://mlsanity.com/paper/1603.08452/) (2016)
- [The narrowing of literature use and the restricted mobility of papers in the sciences](https://mlsanity.com/paper/2204.13736/) (2022)
- [The origin, consequence, and visibility of criticism in science](https://mlsanity.com/paper/2412.02809/) (2024)
- [Citation proximus: the role of social and semantic ties in citing behaviour](https://mlsanity.com/paper/2502.13934/) (2025)

## Abstract (arXiv)

Scientific citations carry rhetorical intent. Scholars may cite prior work positively (supporting), negatively (contrasting), or neutrally (mentioning). As large language models (LLMs) increasingly assist scientific writing, whether they reproduce citations with the same rhetorical intent as humans remains unclear. We introduce a masked-citation task to compare human and LLM-generated citation behavior. For each citation context, an LLM generates a replacement citation sentence, producing a counterfactual corpus directly comparable to human citation. We analyze what, whom, and how models cite, using an LLM-as-a-judge to classify citation intent and a 20-million-edge coauthorship network to measure social distance between cited authors. Across six popular LLMs and 1,746 top NLP conference papers (63k+ contexts, 132k+ citations), three patterns emerge: (1) Compared with human citation, LLMs cite significantly less critically; (2) LLMs over-cite popular and older papers, a tendency amplified for contrasting citations where human writing more often draws on recent, niche work; (3) Whereas humans often cite within their close social network, especially for supporting citations, LLMs tend to draw on more socially distant authors. Together, these differences are double-edged: LLM citation reaches beyond a scholar's close collaborators while being less critical and amplifying visibility bias, reshaping the rhetoric and reach of scientific citation.