stp2y

arXiv:2412.02343v1 Announce Type: new Abstract: In social media, neural network models have been applied to hate speech detection, sentiment analysis, etc., but neural network models are susceptible to adversarial attacks. For instance, in a text classification task, the attacker elaborately introduces perturbations to the original texts that hardly alter the original semantics in order to trick the model into making different predictions. By studying textual adversarial attack methods, the robustness of language models can be evaluated and then improved. Currently, most of the research in this field focuses on English, and there is also a certain amount of research on…

In recent years, artificial intelligence has transformed from an aspirational technology to a driver of manufacturing innovation and efficiency. Understanding both the current landscape and future potential of AI in manufacturing has become essential for strategic decision-making. Recent research shows the manufacturing sector generates over 1,800 petabytes of data annually—more than any other industry—creating both opportunities and challenges for AI implementation.How AI is used in manufacturing todayThe use of AI in manufacturing is accelerating rapidly, with 41 percent of industry executives planning to increase their data and AI spending by more than 25 percent in the coming year, according to…

arXiv:2412.01981v1 Announce Type: new Abstract: Different from its counterpart outcome reward models (ORMs), which evaluate the entire responses, a process reward model (PRM) scores a reasoning trajectory step by step, providing denser and more fine grained rewards. However, training a PRM requires labels annotated at every intermediate step, presenting significant challenges for both manual and automatic data collection. This paper aims to address this challenge. Both theoretically and empirically, we show that an textit{implicit PRM} can be obtained at no additional cost, by simply training an ORM on the cheaper response-level labels. The only assumption is to parameterize the outcome…

[Submitted on 30 Oct 2024 (v1), last revised 3 Dec 2024 (this version, v2)] View a PDF of the paper titled Multi-student Diffusion Distillation for Better One-step Generators, by Yanke Song and 4 other authors View PDF HTML (experimental) Abstract:Diffusion models achieve high-quality sample generation at the cost of a lengthy multistep inference procedure. To overcome this, diffusion distillation techniques produce student generators capable of matching or surpassing the teacher in a single step. However, the student model's inference speed is limited by the size of the teacher architecture, preventing real-time generation for computationally heavy applications. In this work, we…

arXiv:2412.02611v1 Announce Type: cross Abstract: Recently, multimodal large language models (MLLMs), such as GPT-4o, Gemini 1.5 Pro, and Reka Core, have expanded their capabilities to include vision and audio modalities. While these models demonstrate impressive performance across a wide range of audio-visual applications, our proposed DeafTest reveals that MLLMs often struggle with simple tasks humans find trivial: 1) determining which of two sounds is louder, and 2) determining which of two sounds has a higher pitch. Motivated by these observations, we introduce AV-Odyssey Bench, a comprehensive audio-visual benchmark designed to assess whether those MLLMs can truly understand the audio-visual information.…