🔗 Who cites «Large language models surpass human experts in predicting neuroscience results»

20 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · ← back to «Large language models surpass human experts in predicting neuroscience results»

1. Agent Laboratory: Using LLM Agents as Research AssistantsarXiv:2501.04227Samuel Schmidgall, Yusheng Su, Ze Wang, et al.2025 · 139 citations
2. The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Nitay Calderon, Roi Reichart, Rotem Dror2025 · 52 citations
3. AgentRxiv: Towards Collaborative Autonomous ResearcharXiv:2503.18102Samuel Schmidgall, Michael Moor2025 · 39 citations
4. All That Glitters is Not Novel: Plagiarism in AI Generated ResearchAnnual Meeting of the Association for Computational Linguistics (ACL)Tarun Gupta, Danish Pruthi2025 · 23 citations
5. AI4Research: A Survey of Artificial Intelligence for Scientific ResearchScience China Information SciencesQiguang Chen, Mingda Yang, Libo Qin, et al.2025 · 21 citations
6. LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Do Xuan Long, Hai Nguyen Ngoc, Tiviatis Sim, et al.2025 · 14 citations
7. Shakespearean Sparks: The Dance of Hallucination and Creativity in LLMs' Decoding LayersarXiv:2503.02851Zicong He; Boxuan Zhang;, Lu Cheng2025 · 3 citations
8. Trans-EnV: A Framework for Evaluating the Linguistic Robustness of LLMs Against English VarietiesConference on Neural Information Processing Systems (NeurIPS)Jiyoung Lee, Seungho Kim, Jieun Han, et al.2025 · 3 citations

12 more papers of our library cite it; they have no page here yet.

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