日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

One test, many tongues: Surveying language proficiency across the globe

一次测试,多种语言:全球语言能力调查

van Rijn, Pol; Sun, Yue; Lee, Harin; Marjieh, Raja; Sucholutsky, Ilia; Lanzarini, Francesca; André, Elisabeth; Jacoby, Nori

Large language models surpass human experts in predicting neuroscience results

大型语言模型在预测神经科学结果方面超越了人类专家。

Luo, Xiaoliang; Rechardt, Akilles; Sun, Guangzhi; Nejad, Kevin K; Yáñez, Felipe; Yilmaz, Bati; Lee, Kangjoo; Cohen, Alexandra O; Borghesani, Valentina; Pashkov, Anton; Marinazzo, Daniele; Nicholas, Jonathan; Salatiello, Alessandro; Sucholutsky, Ilia; Minervini, Pasquale; Razavi, Sepehr; Rocca, Roberta; Yusifov, Elkhan; Okalova, Tereza; Gu, Nianlong; Ferianc, Martin; Khona, Mikail; Patil, Kaustubh R; Lee, Pui-Shee; Mata, Rui; Myers, Nicholas E; Bizley, Jennifer K; Musslick, Sebastian; Bilgin, Isil Poyraz; Niso, Guiomar; Ales, Justin M; Gaebler, Michael; Ratan Murty, N Apurva; Loued-Khenissi, Leyla; Behler, Anna; Hall, Chloe M; Dafflon, Jessica; Bao, Sherry Dongqi; Love, Bradley C

Explicitly unbiased large language models still form biased associations

即使是明确无偏的大型语言模型,仍然会形成有偏的关联。

Bai, Xuechunzi; Wang, Angelina; Sucholutsky, Ilia; Griffiths, Thomas L

Characterizing the Large-Scale Structure of Multimodal Semantic Networks

刻画多模态语义网络的大规模结构

Marjieh, Raja; van Rijn, Pol; Sucholutsky, Ilia; Lee, Harin; Jacoby, Nori; Griffiths, Thomas L

GPT is an effective tool for multilingual psychological text analysis

GPT是进行多语言心理文本分析的有效工具。

Rathje, Steve; Mirea, Dan-Mircea; Sucholutsky, Ilia; Marjieh, Raja; Robertson, Claire E; Van Bavel, Jay J

Large language models predict human sensory judgments across six modalities

大型语言模型能够预测人类在六种感官模式下的感知判断

Marjieh, Raja; Sucholutsky, Ilia; van Rijn, Pol; Jacoby, Nori; Griffiths, Thomas L

exKidneyBERT: a language model for kidney transplant pathology reports and the crucial role of extended vocabularies

exKidneyBERT:肾移植病理报告的语言模型及扩展词汇表的关键作用

Yang, Tiancheng; Sucholutsky, Ilia; Jen, Kuang-Yu; Schonlau, Matthias

Optimal 1-NN prototypes for pathological geometries

病态几何的最优 1-NN 原型

Sucholutsky, Ilia; Schonlau, Matthias

Pay attention and you won't lose it: a deep learning approach to sequence imputation

集中注意力,你就不会丢失它:一种基于深度学习的序列插补方法

Sucholutsky, Ilia; Narayan, Apurva; Schonlau, Matthias; Fischmeister, Sebastian