no code implementations • 29 Nov 2024 • Angelika Romanou, Negar Foroutan, Anna Sotnikova, Zeming Chen, Sree Harsha Nelaturu, Shivalika Singh, Rishabh Maheshwary, Micol Altomare, Mohamed A. Haggag, Snegha A, Alfonso Amayuelas, Azril Hafizi Amirudin, Viraat Aryabumi, Danylo Boiko, Michael Chang, Jenny Chim, Gal Cohen, Aditya Kumar Dalmia, Abraham Diress, Sharad Duwal, Daniil Dzenhaliou, Daniel Fernando Erazo Florez, Fabian Farestam, Joseph Marvin Imperial, Shayekh Bin Islam, Perttu Isotalo, Maral Jabbarishiviari, Börje F. Karlsson, Eldar Khalilov, Christopher Klamm, Fajri Koto, Dominik Krzemiński, Gabriel Adriano de Melo, Syrielle Montariol, Yiyang Nan, Joel Niklaus, Jekaterina Novikova, Johan Samir Obando Ceron, Debjit Paul, Esther Ploeger, Jebish Purbey, Swati Rajwal, Selvan Sunitha Ravi, Sara Rydell, Roshan Santhosh, Drishti Sharma, Marjana Prifti Skenduli, Arshia Soltani Moakhar, Bardia Soltani Moakhar, Ran Tamir, Ayush Kumar Tarun, Azmine Toushik Wasi, Thenuka Ovin Weerasinghe, Serhan Yilmaz, Mike Zhang, Imanol Schlag, Marzieh Fadaee, Sara Hooker, Antoine Bosselut
The performance differential of large language models (LLM) between languages hinders their effective deployment in many regions, inhibiting the potential economic and societal value of generative AI tools in many communities.
1 code implementation • 23 Sep 2024 • Cong Wang, Zeming Chen, Shanshi Huang
This work demonstrates that MICSim can easily be combined with optimization strategies to perform design space exploration and used for chip-level Transformers CIM accelerators evaluation.
no code implementations • 7 Aug 2024 • Beatriz Borges, Negar Foroutan, Deniz Bayazit, Anna Sotnikova, Syrielle Montariol, Tanya Nazaretzky, Mohammadreza Banaei, Alireza Sakhaeirad, Philippe Servant, Seyed Parsa Neshaei, Jibril Frej, Angelika Romanou, Gail Weiss, Sepideh Mamooler, Zeming Chen, Simin Fan, Silin Gao, Mete Ismayilzada, Debjit Paul, Alexandre Schöpfer, Andrej Janchevski, Anja Tiede, Clarence Linden, Emanuele Troiani, Francesco Salvi, Freya Behrens, Giacomo Orsi, Giovanni Piccioli, Hadrien Sevel, Louis Coulon, Manuela Pineros-Rodriguez, Marin Bonnassies, Pierre Hellich, Puck van Gerwen, Sankalp Gambhir, Solal Pirelli, Thomas Blanchard, Timothée Callens, Toni Abi Aoun, Yannick Calvino Alonso, Yuri Cho, Alberto Chiappa, Antonio Sclocchi, Étienne Bruno, Florian Hofhammer, Gabriel Pescia, Geovani Rizk, Leello Dadi, Lucas Stoffl, Manoel Horta Ribeiro, Matthieu Bovel, Yueyang Pan, Aleksandra Radenovic, Alexandre Alahi, Alexander Mathis, Anne-Florence Bitbol, Boi Faltings, Cécile Hébert, Devis Tuia, François Maréchal, George Candea, Giuseppe Carleo, Jean-Cédric Chappelier, Nicolas Flammarion, Jean-Marie Fürbringer, Jean-Philippe Pellet, Karl Aberer, Lenka Zdeborová, Marcel Salathé, Martin Jaggi, Martin Rajman, Mathias Payer, Matthieu Wyart, Michael Gastpar, Michele Ceriotti, Ola Svensson, Olivier Lévêque, Paolo Ienne, Rachid Guerraoui, Robert West, Sanidhya Kashyap, Valerio Piazza, Viesturs Simanis, Viktor Kuncak, Volkan Cevher, Philippe Schwaller, Sacha Friedli, Patrick Jermann, Tanja Käser, Antoine Bosselut
We investigate the potential scale of this vulnerability by measuring the degree to which AI assistants can complete assessment questions in standard university-level STEM courses.
no code implementations • 17 Jun 2024 • Jiaqi Wang, Yuhang Zang, Pan Zhang, Tao Chu, Yuhang Cao, Zeyi Sun, Ziyu Liu, Xiaoyi Dong, Tong Wu, Dahua Lin, Zeming Chen, Zhi Wang, Lingchen Meng, Wenhao Yao, Jianwei Yang, Sihong Wu, Zhineng Chen, Zuxuan Wu, Yu-Gang Jiang, Peixi Wu, Bosong Chai, Xuan Nie, Longquan Yan, Zeyu Wang, Qifan Zhou, Boning Wang, Jiaqi Huang, Zunnan Xu, Xiu Li, Kehong Yuan, Yanyan Zu, Jiayao Ha, Qiong Gao, Licheng Jiao
2) Open Vocabulary Object Detection: This track goes a step further, requiring algorithms to detect objects from an open set of categories, including unknown objects.
1 code implementation • 12 Mar 2024 • Tianqing Fang, Zeming Chen, Yangqiu Song, Antoine Bosselut
Event commonsense reasoning requires the ability to reason about the relationship between events, as well as infer implicit context underlying that relationship.
1 code implementation • 12 Dec 2023 • Zeming Chen, Wenwei Zhang, Xinjiang Wang, Kai Chen, Zhi Wang
While the pseudo-label method has demonstrated considerable success in semi-supervised object detection tasks, this paper uncovers notable limitations within this approach.
Ranked #1 on
Semi-Supervised Object Detection
on COCO 100% labeled data
(using extra training data)
1 code implementation • 27 Nov 2023 • Zeming Chen, Alejandro Hernández Cano, Angelika Romanou, Antoine Bonnet, Kyle Matoba, Francesco Salvi, Matteo Pagliardini, Simin Fan, Andreas Köpf, Amirkeivan Mohtashami, Alexandre Sallinen, Alireza Sakhaeirad, Vinitra Swamy, Igor Krawczuk, Deniz Bayazit, Axel Marmet, Syrielle Montariol, Mary-Anne Hartley, Martin Jaggi, Antoine Bosselut
Large language models (LLMs) can potentially democratize access to medical knowledge.
Ranked #1 on
Multiple Choice Question Answering (MCQA)
on MedMCQA
(Dev Set (Acc-%) metric)
1 code implementation • 4 Oct 2023 • Deniz Bayazit, Negar Foroutan, Zeming Chen, Gail Weiss, Antoine Bosselut
In this work, we investigate whether pretrained language models contain various knowledge-critical subnetworks: particular sparse computational subgraphs that can, if removed, precisely suppress specific knowledge the model has memorized.
1 code implementation • 28 May 2023 • Yu Fei, Yifan Hou, Zeming Chen, Antoine Bosselut
In this work, we define a typology for three types of label biases in ICL for text classification: vanilla-label bias, context-label bias, and domain-label bias (which we conceptualize and detect for the first time).
no code implementations • NeurIPS 2023 • Zeming Chen, Gail Weiss, Eric Mitchell, Asli Celikyilmaz, Antoine Bosselut
In the outer loop, the model learns to use the updated weights to reproduce and answer reasoning questions about the memorized knowledge.
1 code implementation • 20 Dec 2022 • Zeming Chen, Qiyue Gao, Antoine Bosselut, Ashish Sabharwal, Kyle Richardson
However, high-quality counterfactual data is scarce for most tasks and not easily generated at scale.
no code implementations • NAACL 2022 • Zeming Chen, Qiyue Gao
In the age of large transformer language models, linguistic evaluation play an important role in diagnosing models' abilities and limitations on natural language understanding.
1 code implementation • 3 Dec 2021 • Zeming Chen, Qiyue Gao
We propose a methodology for probing linguistic information for logical inference in pre-trained language model representations.
1 code implementation • Joint Conference on Lexical and Computational Semantics 2021 • Zeming Chen, Qiyue Gao, Lawrence S. Moss
Deep learning (DL) based language models achieve high performance on various benchmarks for Natural Language Inference (NLI).
Ranked #1 on
Natural Language Inference
on MED
1 code implementation • IWCS (ACL) 2021 • Zeming Chen, Qiyue Gao
Dependency parsing is a tool widely used in the field of Natural language processing and computational linguistics.
no code implementations • ACL (NALOMA, IWCS) 2021 • Zeming Chen
We show and attempt to explain that our model outperforms existing models on MED.