Large language models surpass human experts in predicting neuroscience results

no code implementations • 4 Mar 2024 • Xiaoliang Luo, Akilles Rechardt, Guangzhi Sun, Kevin K. Nejad, Felipe Yáñez, Bati Yilmaz, Kangjoo Lee, Alexandra O. Cohen, Valentina Borghesani, Anton Pashkov, Daniele Marinazzo, Jonathan Nicholas, Alessandro Salatiello, Ilia Sucholutsky, Pasquale Minervini, Sepehr Razavi, Roberta Rocca, Elkhan Yusifov, Tereza Okalova, Nianlong Gu, Martin Ferianc, Mikail Khona, Kaustubh R. Patil, Pui-Shee Lee, Rui Mata, Nicholas E. Myers, Jennifer K Bizley, Sebastian Musslick, Isil Poyraz Bilgin, Guiomar Niso, Justin M. Ales, Michael Gaebler, N Apurva Ratan Murty, Leyla Loued-Khenissi, Anna Behler, Chloe M. Hall, Jessica Dafflon, Sherry Dongqi Bao, Bradley C. Love

LLMs trained on the vast scientific literature could potentially integrate noisy yet interrelated findings to forecast novel results better than human experts.

Using novel data and ensemble models to improve automated labeling of Sustainable Development Goals

1 code implementation • 25 Jan 2023 • Dirk U. Wulff, Dominik S. Meier, Rui Mata

A number of labeling systems based on text have been proposed to help monitor work on the United Nations (UN) Sustainable Development Goals (SDGs).

Specificity

text2sdg: An R package to Monitor Sustainable Development Goals from Text

1 code implementation • 12 Oct 2021 • Dominik S. Meier, Rui Mata, Dirk U. Wulff

Monitoring progress on the United Nations Sustainable Development Goals (SDGs) is important for both academic and non-academic organizations.