A new study reveals that current language models exhibit a significant citation monoculture, meaning they tend to cite the same narrow subset of papers even when presented with a diverse range of options. This phenomenon persists even when the models are provided with real papers and their abstracts, and the citations themselves are accurate. The research suggests that this shared preference map, driven by paper content, is a key factor, indicating that simply mixing vendors or improving retrieval mechanisms is insufficient to broaden AI-generated citations. AI
IMPACT This research highlights a potential bias in AI-driven research discovery, impacting how scientific attention is distributed and potentially limiting exposure to novel or niche findings.
RANK_REASON The cluster is based on an academic paper analyzing the behavior of language models.
- Deep SerpApi
- Google Ai Overviews
- Perplexity
- Scrapeless
- alphaXiv
- CatalyzeX Code Finder for Papers
- Connected Papers
- DagsHub
- Gotit.pub
- GPT-5
- Hugging Face
- Litmaps
- scite Smart Citations
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