Zobrazeno 1 - 10
of 532
pro vyhledávání: '"A. Andriushchenko"'
Autor:
A. S. Tomyshev, A. Dudina, D. Romanov, E. Ilina, M. Magomedagaev, G. Kostyuk, A. Andriushchenko, A. Smulevich, I. Lebedeva
Publikováno v:
European Psychiatry, Vol 67, Pp S620-S620 (2024)
Introduction There is strong evidence that delusions are associated with cortical and subcortical structural alterations. However, whether these abnormalities differ in different types of delusions within schizophrenia spectrum disorders remains uncl
Externí odkaz:
https://doaj.org/article/0459afc61b31461680d4460ce6b2d857
Autor:
A. S. Tomyshev, Y. Panikratova, E. Abdullina, I. Lebedeva, P. Iuzbashian, K. Dmitrenko, G. Kostyuk, A. Andriushchenko, D. Romanov, A. Smulevich
Publikováno v:
European Psychiatry, Vol 66, Pp S913-S914 (2023)
Introduction There is growing evidence to suggest that delusions in schizophrenia-spectrum disorders are associated with altered brain connectivity. Disruptions in long association fibers, such as the superior longitudinal fasciculus, are among the m
Externí odkaz:
https://doaj.org/article/80df62e0ba824a7dbf438a54794138c0
Autor:
Andriushchenko, Maksym, Souly, Alexandra, Dziemian, Mateusz, Duenas, Derek, Lin, Maxwell, Wang, Justin, Hendrycks, Dan, Zou, Andy, Kolter, Zico, Fredrikson, Matt, Winsor, Eric, Wynne, Jerome, Gal, Yarin, Davies, Xander
The robustness of LLMs to jailbreak attacks, where users design prompts to circumvent safety measures and misuse model capabilities, has been studied primarily for LLMs acting as simple chatbots. Meanwhile, LLM agents -- which use external tools and
Externí odkaz:
http://arxiv.org/abs/2410.09024
Autor:
Y. Zorkina, T. Syunyakov, A. Andriushchenko, O. Abramova, M. Kurmishev, G. Kostyuk, A. Morozova
Publikováno v:
European Psychiatry, Vol 65, Pp S172-S172 (2022)
Introduction Mild cognitive impairment (MCI) represent a state of cognitive function between normal aging and dementia and does not always progress to dementia. Neuroinflammation has a key role in the pathogenesis of neurodegeneration. Determining th
Externí odkaz:
https://doaj.org/article/139d6b7ff95443dda0b599a818499090
Markov decision processes (MDPs) provide a fundamental model for sequential decision making under process uncertainty. A classical synthesis task is to compute for a given MDP a winning policy that achieves a desired specification. However, at design
Externí odkaz:
http://arxiv.org/abs/2407.12552
Refusal training is widely used to prevent LLMs from generating harmful, undesirable, or illegal outputs. We reveal a curious generalization gap in the current refusal training approaches: simply reformulating a harmful request in the past tense (e.g
Externí odkaz:
http://arxiv.org/abs/2407.11969
Autor:
Zou, Andy, Phan, Long, Wang, Justin, Duenas, Derek, Lin, Maxwell, Andriushchenko, Maksym, Wang, Rowan, Kolter, Zico, Fredrikson, Matt, Hendrycks, Dan
AI systems can take harmful actions and are highly vulnerable to adversarial attacks. We present an approach, inspired by recent advances in representation engineering, that interrupts the models as they respond with harmful outputs with "circuit bre
Externí odkaz:
http://arxiv.org/abs/2406.04313
In-context learning (ICL) allows LLMs to learn from examples without changing their weights: this is a particularly promising capability for long-context LLMs that can potentially learn from many examples. Recently, Lin et al. (2024) proposed URIAL,
Externí odkaz:
http://arxiv.org/abs/2405.19874
Autor:
Andriushchenko, Roman, Bork, Alexander, Budde, Carlos E., Češka, Milan, Grover, Kush, Hahn, Ernst Moritz, Hartmanns, Arnd, Israelsen, Bryant, Jansen, Nils, Jeppson, Joshua, Junges, Sebastian, Köhl, Maximilian A., Könighofer, Bettina, Křetínský, Jan, Meggendorfer, Tobias, Parker, David, Pranger, Stefan, Quatmann, Tim, Ruijters, Enno, Taylor, Landon, Volk, Matthias, Weininger, Maximilian, Zhang, Zhen
The analysis of formal models that include quantitative aspects such as timing or probabilistic choices is performed by quantitative verification tools. Broad and mature tool support is available for computing basic properties such as expected reward
Externí odkaz:
http://arxiv.org/abs/2405.13583
Autor:
Rando, Javier, Croce, Francesco, Mitka, Kryštof, Shabalin, Stepan, Andriushchenko, Maksym, Flammarion, Nicolas, Tramèr, Florian
Large language models are aligned to be safe, preventing users from generating harmful content like misinformation or instructions for illegal activities. However, previous work has shown that the alignment process is vulnerable to poisoning attacks.
Externí odkaz:
http://arxiv.org/abs/2404.14461