Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Lyzhov, Alexander"'
Language models (LMs) have been shown to behave unexpectedly post-deployment. For example, new jailbreaks continually arise, allowing model misuse, despite extensive red-teaming and adversarial training from developers. Given most model queries are u
Externí odkaz:
http://arxiv.org/abs/2406.15518
Autor:
McKenzie, Ian R., Lyzhov, Alexander, Pieler, Michael, Parrish, Alicia, Mueller, Aaron, Prabhu, Ameya, McLean, Euan, Kirtland, Aaron, Ross, Alexis, Liu, Alisa, Gritsevskiy, Andrew, Wurgaft, Daniel, Kauffman, Derik, Recchia, Gabriel, Liu, Jiacheng, Cavanagh, Joe, Weiss, Max, Huang, Sicong, Droid, The Floating, Tseng, Tom, Korbak, Tomasz, Shen, Xudong, Zhang, Yuhui, Zhou, Zhengping, Kim, Najoung, Bowman, Samuel R., Perez, Ethan
Publikováno v:
Transactions on Machine Learning Research (TMLR), 10/2023, https://openreview.net/forum?id=DwgRm72GQF
Work on scaling laws has found that large language models (LMs) show predictable improvements to overall loss with increased scale (model size, training data, and compute). Here, we present evidence for the claim that LMs may show inverse scaling, or
Externí odkaz:
http://arxiv.org/abs/2306.09479
Autor:
Stastny, Julian, Riché, Maxime, Lyzhov, Alexander, Treutlein, Johannes, Dafoe, Allan, Clifton, Jesse
Cooperation in settings where agents have both common and conflicting interests (mixed-motive environments) has recently received considerable attention in multi-agent learning. However, the mixed-motive environments typically studied have a single c
Externí odkaz:
http://arxiv.org/abs/2111.13872
Test-time data augmentation$-$averaging the predictions of a machine learning model across multiple augmented samples of data$-$is a widely used technique that improves the predictive performance. While many advanced learnable data augmentation techn
Externí odkaz:
http://arxiv.org/abs/2002.09103
Publikováno v:
Eighth International Conference on Learning Representations (ICLR 2020)
Uncertainty estimation and ensembling methods go hand-in-hand. Uncertainty estimation is one of the main benchmarks for assessment of ensembling performance. At the same time, deep learning ensembles have provided state-of-the-art results in uncertai
Externí odkaz:
http://arxiv.org/abs/2002.06470