Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants

Autor: Borges, Beatriz, Foroutan, Negar, Bayazit, Deniz, Sotnikova, Anna, Montariol, Syrielle, Nazaretzky, Tanya, Banaei, Mohammadreza, Sakhaeirad, Alireza, Servant, Philippe, Neshaei, Seyed Parsa, Frej, Jibril, Romanou, Angelika, Weiss, Gail, Mamooler, Sepideh, Chen, Zeming, Fan, Simin, Gao, Silin, Ismayilzada, Mete, Paul, Debjit, Schöpfer, Alexandre, Janchevski, Andrej, Tiede, Anja, Linden, Clarence, Troiani, Emanuele, Salvi, Francesco, Behrens, Freya, Orsi, Giacomo, Piccioli, Giovanni, Sevel, Hadrien, Coulon, Louis, Pineros-Rodriguez, Manuela, Bonnassies, Marin, Hellich, Pierre, van Gerwen, Puck, Gambhir, Sankalp, Pirelli, Solal, Blanchard, Thomas, Callens, Timothée, Aoun, Toni Abi, Alonso, Yannick Calvino, Cho, Yuri, Chiappa, Alberto, Sclocchi, Antonio, Bruno, Étienne, Hofhammer, Florian, Pescia, Gabriel, Rizk, Geovani, Dadi, Leello, Stoffl, Lucas, Ribeiro, Manoel Horta, Bovel, Matthieu, Pan, Yueyang, Radenovic, Aleksandra, Alahi, Alexandre, Mathis, Alexander, Bitbol, Anne-Florence, Faltings, Boi, Hébert, Cécile, Tuia, Devis, Maréchal, François, Candea, George, Carleo, Giuseppe, Chappelier, Jean-Cédric, Flammarion, Nicolas, Fürbringer, Jean-Marie, Pellet, Jean-Philippe, Aberer, Karl, Zdeborová, Lenka, Salathé, Marcel, Jaggi, Martin, Rajman, Martin, Payer, Mathias, Wyart, Matthieu, Gastpar, Michael, Ceriotti, Michele, Svensson, Ola, Lévêque, Olivier, Ienne, Paolo, Guerraoui, Rachid, West, Robert, Kashyap, Sanidhya, Piazza, Valerio, Simanis, Viesturs, Kuncak, Viktor, Cevher, Volkan, Schwaller, Philippe, Friedli, Sacha, Jermann, Patrick, Kaser, Tanja, Bosselut, Antoine

AI assistants are being increasingly used by students enrolled in higher education institutions. While these tools provide opportunities for improved teaching and education, they also pose significant challenges for assessment and learning outcomes.

Externí odkaz: http://arxiv.org/abs/2408.11841

DiffuCOMET: Contextual Commonsense Knowledge Diffusion

Autor: Gao, Silin, Ismayilzada, Mete, Zhao, Mengjie, Wakaki, Hiromi, Mitsufuji, Yuki, Bosselut, Antoine

Inferring contextually-relevant and diverse commonsense to understand narratives remains challenging for knowledge models. In this work, we develop a series of knowledge models, DiffuCOMET, that leverage diffusion to learn to reconstruct the implicit

Externí odkaz: http://arxiv.org/abs/2402.17011

Efficient Tool Use with Chain-of-Abstraction Reasoning

Autor: Gao, Silin, Dwivedi-Yu, Jane, Yu, Ping, Tan, Xiaoqing Ellen, Pasunuru, Ramakanth, Golovneva, Olga, Sinha, Koustuv, Celikyilmaz, Asli, Bosselut, Antoine, Wang, Tianlu

To achieve faithful reasoning that aligns with human expectations, large language models (LLMs) need to ground their reasoning to real-world knowledge (e.g., web facts, math and physical rules). Tools help LLMs access this external knowledge, but the

Externí odkaz: http://arxiv.org/abs/2401.17464

Efficient Gridless DoA Estimation Method of Non-uniform Linear Arrays with Applications in Automotive Radars

Autor: Gao, Silin, Zhang, Zhe, Wang, Muhan, Zhang, Yan, Zhao, Jie, Zhang, Bingchen, Wang, Yue, Wu, Yirong

This paper focuses on the gridless direction-of-arrival (DoA) estimation for data acquired by non-uniform linear arrays (NLAs) in automotive applications. Atomic norm minimization (ANM) is a promising gridless sparse recovery algorithm under the Toep

Externí odkaz: http://arxiv.org/abs/2303.04374

ComFact: A Benchmark for Linking Contextual Commonsense Knowledge

Autor: Gao, Silin, Hwang, Jena D., Kanno, Saya, Wakaki, Hiromi, Mitsufuji, Yuki, Bosselut, Antoine

Understanding rich narratives, such as dialogues and stories, often requires natural language processing systems to access relevant knowledge from commonsense knowledge graphs. However, these systems typically retrieve facts from KGs using simple heu

Externí odkaz: http://arxiv.org/abs/2210.12678