Papers by Floriano Tori
Large Language Models Reflect Human Citation Patterns with a Heightened Citation Bias (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) can help in citation generation but can also amplify existing biases, such as the Matthew effect, and introduce new ones, potentially skewing scientific knowledge dissemination. |
| Approach: | They propose to use large language models to generate scholarly references for in-text citations in papers published after GPT-4's knowledge cut-off date. |
| Outcome: | The proposed model can generate scholarly references for in-text citations, but without the aid of web browsing or a search engine, the results show a similarity between human and LLM citation patterns, but with a more pronounced high citation bias. |