Zobrazeno 1 - 10
of 503
pro vyhledávání: '"Langer,Stefan"'
Autor:
Riedler, Monica, Langer, Stefan
Large Language Models (LLMs) have demonstrated impressive capabilities in answering questions, but they lack domain-specific knowledge and are prone to hallucinations. Retrieval Augmented Generation (RAG) is one approach to address these challenges,
Externí odkaz:
http://arxiv.org/abs/2410.21943
Physics-informed neural networks have gained popularity as a deep-learning based method for solving problems governed by partial differential equations. Especially for engineering applications, this new method seems to be promising since it can solve
Externí odkaz:
http://arxiv.org/abs/2408.17364
Ontologies are formal representations of knowledge in specific domains that provide a structured framework for organizing and understanding complex information. Creating ontologies, however, is a complex and time-consuming endeavor. ChEBI is a well-k
Externí odkaz:
http://arxiv.org/abs/2407.21708
Autor:
Weber, Ingo, Linka, Hendrik, Mertens, Daniel, Muryshkin, Tamara, Opgenoorth, Heinrich, Langer, Stefan
Since OpenAI's release of ChatGPT, generative AI has received significant attention across various domains. These AI-based chat systems have the potential to enhance the productivity of knowledge workers in diverse tasks. However, the use of free pub
Externí odkaz:
http://arxiv.org/abs/2403.00039
Publikováno v:
C.&Fl. 270 (2024)
The numerical approximation of solutions to the compressible Euler and Navier-Stokes equations is a crucial but challenging task with relevance in various fields of science and engineering. Recently, methods from deep learning have been successfully
Externí odkaz:
http://arxiv.org/abs/2307.14045
Autor:
Yaseen, Usama, Langer, Stefan
This paper presents our findings from participating in the multilingual acronym extraction shared task SDU@AAAI-22. The task consists of acronym extraction from documents in 6 languages within scientific and legal domains. To address multilingual acr
Externí odkaz:
http://arxiv.org/abs/2206.15221
Publikováno v:
In JPRAS Open December 2024 42:197-207
Publikováno v:
In JPRAS Open March 2025 43:347-356
Autor:
Dhole, Kaustubh D., Gangal, Varun, Gehrmann, Sebastian, Gupta, Aadesh, Li, Zhenhao, Mahamood, Saad, Mahendiran, Abinaya, Mille, Simon, Shrivastava, Ashish, Tan, Samson, Wu, Tongshuang, Sohl-Dickstein, Jascha, Choi, Jinho D., Hovy, Eduard, Dusek, Ondrej, Ruder, Sebastian, Anand, Sajant, Aneja, Nagender, Banjade, Rabin, Barthe, Lisa, Behnke, Hanna, Berlot-Attwell, Ian, Boyle, Connor, Brun, Caroline, Cabezudo, Marco Antonio Sobrevilla, Cahyawijaya, Samuel, Chapuis, Emile, Che, Wanxiang, Choudhary, Mukund, Clauss, Christian, Colombo, Pierre, Cornell, Filip, Dagan, Gautier, Das, Mayukh, Dixit, Tanay, Dopierre, Thomas, Dray, Paul-Alexis, Dubey, Suchitra, Ekeinhor, Tatiana, Di Giovanni, Marco, Goyal, Tanya, Gupta, Rishabh, Hamla, Louanes, Han, Sang, Harel-Canada, Fabrice, Honore, Antoine, Jindal, Ishan, Joniak, Przemyslaw K., Kleyko, Denis, Kovatchev, Venelin, Krishna, Kalpesh, Kumar, Ashutosh, Langer, Stefan, Lee, Seungjae Ryan, Levinson, Corey James, Liang, Hualou, Liang, Kaizhao, Liu, Zhexiong, Lukyanenko, Andrey, Marivate, Vukosi, de Melo, Gerard, Meoni, Simon, Meyer, Maxime, Mir, Afnan, Moosavi, Nafise Sadat, Muennighoff, Niklas, Mun, Timothy Sum Hon, Murray, Kenton, Namysl, Marcin, Obedkova, Maria, Oli, Priti, Pasricha, Nivranshu, Pfister, Jan, Plant, Richard, Prabhu, Vinay, Pais, Vasile, Qin, Libo, Raji, Shahab, Rajpoot, Pawan Kumar, Raunak, Vikas, Rinberg, Roy, Roberts, Nicolas, Rodriguez, Juan Diego, Roux, Claude, S., Vasconcellos P. H., Sai, Ananya B., Schmidt, Robin M., Scialom, Thomas, Sefara, Tshephisho, Shamsi, Saqib N., Shen, Xudong, Shi, Haoyue, Shi, Yiwen, Shvets, Anna, Siegel, Nick, Sileo, Damien, Simon, Jamie, Singh, Chandan, Sitelew, Roman, Soni, Priyank, Sorensen, Taylor, Soto, William, Srivastava, Aman, Srivatsa, KV Aditya, Sun, Tony, T, Mukund Varma, Tabassum, A, Tan, Fiona Anting, Teehan, Ryan, Tiwari, Mo, Tolkiehn, Marie, Wang, Athena, Wang, Zijian, Wang, Gloria, Wang, Zijie J., Wei, Fuxuan, Wilie, Bryan, Winata, Genta Indra, Wu, Xinyi, Wydmański, Witold, Xie, Tianbao, Yaseen, Usama, Yee, Michael A., Zhang, Jing, Zhang, Yue
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on. In this paper, we present NL-Augmenter, a new participatory Python
Externí odkaz:
http://arxiv.org/abs/2112.02721