Zobrazeno 1 - 10
of 23 522
pro vyhledávání: '"YANAI, A."'
Autor:
He, Yan, Drozd, Vasyl, Ekawa, Hiroyuki, Escrig, Samuel, Gao, Yiming, Kasagi, Ayumi, Liu, Enqiang, Muneem, Abdul, Nakagawa, Manami, Nakazawa, Kazuma, Rappold, Christophe, Saito, Nami, Saito, Takehiko R., Sugimoto, Shohei, Taki, Masato, Tanaka, Yoshiki K., Wang, He, Yanai, Ayari, Yoshida, Junya, Zhang, Hongfei
A novel method was developed to detect double-$\Lambda$ hypernuclear events in nuclear emulsions using machine learning techniques. The object detection model, the Mask R-CNN, was trained using images generated by Monte Carlo simulations, image proce
Externí odkaz:
http://arxiv.org/abs/2409.01657
Autor:
Sainz, Oscar, García-Ferrero, Iker, Jacovi, Alon, Campos, Jon Ander, Elazar, Yanai, Agirre, Eneko, Goldberg, Yoav, Chen, Wei-Lin, Chim, Jenny, Choshen, Leshem, D'Amico-Wong, Luca, Dell, Melissa, Fan, Run-Ze, Golchin, Shahriar, Li, Yucheng, Liu, Pengfei, Pahwa, Bhavish, Prabhu, Ameya, Sharma, Suryansh, Silcock, Emily, Solonko, Kateryna, Stap, David, Surdeanu, Mihai, Tseng, Yu-Min, Udandarao, Vishaal, Wang, Zengzhi, Xu, Ruijie, Yang, Jinglin
The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as situations where evaluation data is included in pre-training corpora u
Externí odkaz:
http://arxiv.org/abs/2407.21530
Autor:
Sharma, Rashi, Okada, Hiroyuki, Oba, Tatsumi, Subramanian, Karthikk, Yanai, Naoto, Pranata, Sugiri
The Industrial Control System (ICS) environment encompasses a wide range of intricate communication protocols, posing substantial challenges for Security Operations Center (SOC) analysts tasked with monitoring, interpreting, and addressing network ac
Externí odkaz:
http://arxiv.org/abs/2407.15428
Autor:
Antoniades, Antonis, Wang, Xinyi, Elazar, Yanai, Amayuelas, Alfonso, Albalak, Alon, Zhang, Kexun, Wang, William Yang
Despite the proven utility of large language models (LLMs) in real-world applications, there remains a lack of understanding regarding how they leverage their large-scale pretraining text corpora to achieve such capabilities. In this work, we investi
Externí odkaz:
http://arxiv.org/abs/2407.14985
Recent work on evaluating the diversity of text generated by LLMs has focused on word-level features. Here we offer an analysis of syntactic features to characterize general repetition in models, beyond frequent n-grams. Specifically, we define synta
Externí odkaz:
http://arxiv.org/abs/2407.00211
How novel are texts generated by language models (LMs) relative to their training corpora? In this work, we investigate the extent to which modern LMs generate $n$-grams from their training data, evaluating both (i) the probability LMs assign to comp
Externí odkaz:
http://arxiv.org/abs/2406.13069
Most works on gender bias focus on intrinsic bias -- removing traces of information about a protected group from the model's internal representation. However, these works are often disconnected from the impact of such debiasing on downstream applicat
Externí odkaz:
http://arxiv.org/abs/2406.00787
Autor:
Albalak, Alon, Elazar, Yanai, Xie, Sang Michael, Longpre, Shayne, Lambert, Nathan, Wang, Xinyi, Muennighoff, Niklas, Hou, Bairu, Pan, Liangming, Jeong, Haewon, Raffel, Colin, Chang, Shiyu, Hashimoto, Tatsunori, Wang, William Yang
A major factor in the recent success of large language models is the use of enormous and ever-growing text datasets for unsupervised pre-training. However, naively training a model on all available data may not be optimal (or feasible), as the qualit
Externí odkaz:
http://arxiv.org/abs/2402.16827
Autor:
Lyu, Qing, Shridhar, Kumar, Malaviya, Chaitanya, Zhang, Li, Elazar, Yanai, Tandon, Niket, Apidianaki, Marianna, Sachan, Mrinmaya, Callison-Burch, Chris
Accurately gauging the confidence level of Large Language Models' (LLMs) predictions is pivotal for their reliable application. However, LLMs are often uncalibrated inherently and elude conventional calibration techniques due to their proprietary nat
Externí odkaz:
http://arxiv.org/abs/2402.13904
Autor:
Groeneveld, Dirk, Beltagy, Iz, Walsh, Pete, Bhagia, Akshita, Kinney, Rodney, Tafjord, Oyvind, Jha, Ananya Harsh, Ivison, Hamish, Magnusson, Ian, Wang, Yizhong, Arora, Shane, Atkinson, David, Authur, Russell, Chandu, Khyathi Raghavi, Cohan, Arman, Dumas, Jennifer, Elazar, Yanai, Gu, Yuling, Hessel, Jack, Khot, Tushar, Merrill, William, Morrison, Jacob, Muennighoff, Niklas, Naik, Aakanksha, Nam, Crystal, Peters, Matthew E., Pyatkin, Valentina, Ravichander, Abhilasha, Schwenk, Dustin, Shah, Saurabh, Smith, Will, Strubell, Emma, Subramani, Nishant, Wortsman, Mitchell, Dasigi, Pradeep, Lambert, Nathan, Richardson, Kyle, Zettlemoyer, Luke, Dodge, Jesse, Lo, Kyle, Soldaini, Luca, Smith, Noah A., Hajishirzi, Hannaneh
Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important det
Externí odkaz:
http://arxiv.org/abs/2402.00838