Zobrazeno 1 - 10
of 95
pro vyhledávání: '"Dolamic, Ljiljana"'
Many-to-one neural machine translation systems improve over one-to-one systems when training data is scarce. In this paper, we design and test a novel algorithm for selecting the language of minibatches when training such systems. The algorithm chang
Externí odkaz:
http://arxiv.org/abs/2410.04147
With the emergence of widely available powerful LLMs, disinformation generated by large Language Models (LLMs) has become a major concern. Historically, LLM detectors have been touted as a solution, but their effectiveness in the real world is still
Externí odkaz:
http://arxiv.org/abs/2409.03291
Assessing the robustness of multimodal models against adversarial examples is an important aspect for the safety of its users. We craft L0-norm perturbation attacks on the preprocessed input images. We launch them in a black-box setup against four mu
Externí odkaz:
http://arxiv.org/abs/2407.18251
Prompting and Multiple Choices Questions (MCQ) have become the preferred approach to assess the capabilities of Large Language Models (LLMs), due to their ease of manipulation and evaluation. Such experimental appraisals have pointed toward the LLMs'
Externí odkaz:
http://arxiv.org/abs/2406.14986
Neural Machine Translation (NMT) models have been shown to be vulnerable to adversarial attacks, wherein carefully crafted perturbations of the input can mislead the target model. In this paper, we introduce ACT, a novel adversarial attack framework
Externí odkaz:
http://arxiv.org/abs/2308.15246
In this paper, we propose an optimization-based adversarial attack against Neural Machine Translation (NMT) models. First, we propose an optimization problem to generate adversarial examples that are semantically similar to the original sentences but
Externí odkaz:
http://arxiv.org/abs/2306.08492
Autor:
Wolleb, Benoist, Silvestri, Romain, Vernikos, Giorgos, Dolamic, Ljiljana, Popescu-Belis, Andrei
Subword tokenization is the de facto standard for tokenization in neural language models and machine translation systems. Three advantages are frequently cited in favor of subwords: shorter encoding of frequent tokens, compositionality of subwords, a
Externí odkaz:
http://arxiv.org/abs/2306.01393
Whenever applicable, the Stochastic Gradient Descent (SGD) has shown itself to be unreasonably effective. Instead of underperforming and getting trapped in local minima due to the batch noise, SGD leverages it to learn to generalize better and find m
Externí odkaz:
http://arxiv.org/abs/2306.09991
Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms but also to a drastic increase in computational power used to train t
Externí odkaz:
http://arxiv.org/abs/2304.13540
Autor:
Kucharavy, Andrei, Schillaci, Zachary, Maréchal, Loïc, Würsch, Maxime, Dolamic, Ljiljana, Sabonnadiere, Remi, David, Dimitri Percia, Mermoud, Alain, Lenders, Vincent
Generative Language Models gained significant attention in late 2022 / early 2023, notably with the introduction of models refined to act consistently with users' expectations of interactions with AI (conversational models). Arguably the focal point
Externí odkaz:
http://arxiv.org/abs/2303.12132