Author: Stella Ross

Authors: Chengzhi Zhong、Fei Cheng、Qianying Liu、Junfeng Jiang、Zhen Wan、Chenhui Chu、Yugo Murawaki、Sadao Kurohashi Paper: https://arxiv.org/abs/2408.10811 Introduction Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP), predominantly focusing on the English language. However, the performance of these English-centric models often declines when applied to non-English languages, raising concerns about cultural biases and language representation. This study, conducted by researchers from Kyoto University, the National Institute of Informatics, and the University of Tokyo, delves into the internal workings of non-English-centric LLMs to understand the language they ‘think’ in during intermediate processing layers. Related Work Multilingual Large Language Models The development of…

Authors: Jiyang Qiu、Xinbei Ma、Zhuosheng Zhang、Hai Zhao Paper: https://arxiv.org/abs/2408.10722 Introduction The rapid advancements in large language models (LLMs) have revolutionized the field of natural language processing (NLP). These models, with their remarkable generative capabilities, have become indispensable tools for a wide range of tasks. However, their increasing dependency also makes them vulnerable to backdoor attacks. This paper introduces MEGen, a novel approach to embedding generative backdoors into LLMs through model editing. The goal is to create customized backdoors for NLP tasks with minimal side effects, ensuring high attack success rates while maintaining the model’s performance on clean data. Related Work Large…

Authors: Yanbo Ding、Shaobin Zhuang、Kunchang Li、Zhengrong Yue、Yu Qiao、Yali Wang Paper: https://arxiv.org/abs/2408.10605 Introduction In recent years, the field of text-to-image generation has seen significant advancements, with models like Stable Diffusion and DALL-E pushing the boundaries of what is possible. However, these models often struggle with generating images that contain multiple objects with complex spatial relationships in a 3D world. This limitation is particularly evident when precise control over 3D attributes such as object orientation, spatial relationships, and camera views is required. To address this challenge, the authors introduce MUSES, a novel AI system designed for 3D-controllable image generation from user queries. MUSES…

Authors: Syed Rifat Raiyan、Zibran Zarif Amio、Sabbir Ahmed Paper: https://arxiv.org/abs/2408.10360 Hand shadow puppetry, also known as shadowgraphy or ombromanie, is a captivating form of theatrical art and storytelling. However, this ancient art form is on the brink of extinction due to the dwindling number of practitioners and changing entertainment standards. To address this issue, researchers Syed Rifat Raiyan, Zibran Zarif Amio, and Sabbir Ahmed have introduced HaSPeR, a novel dataset aimed at preserving and proliferating hand shadow puppetry. This blog delves into their study, exploring the dataset, methodologies, experimental design, and findings. 1. Introduction Background Hand shadow puppetry involves creating silhouettes…

Authors: Toshihide Ubukata、Jialong Li、Kenji Tei Paper: https://arxiv.org/abs/2408.10266 Introduction Diffusion models have recently emerged as a powerful class of generative models in the field of Generative Artificial Intelligence (GenAI). These models utilize stochastic processes to transform random noise into high-quality data through iterative denoising. Initially, diffusion models demonstrated their capabilities in image-related tasks such as generation, restoration, enhancement, and editing. The fundamental principle involves introducing noise to training data and learning to reverse this process through iterative denoising, effectively capturing complex data distributions. Technically, diffusion models use stochastic differential equations to progressively refine noisy inputs into coherent outputs, enabling accurate modeling…

Authors: Shangxi Wu、Jitao Sang Paper: https://arxiv.org/abs/2408.10334 Introduction Background In recent years, large language models (LLMs) have made significant strides in the field of code generation. These advancements have led to widespread adoption by developers and researchers who use these models to assist in software development. However, as reliance on these models grows, so do the associated security risks. Traditional deep learning robustness issues, such as backdoor attacks, adversarial attacks, and data poisoning, also plague code generation models, posing significant threats to their security. Problem Statement This paper introduces a novel game-theoretic model that addresses security issues in code generation scenarios.…

Authors: Xingyue Lin、Xingjian Hu、Shuai Peng、Jianhua Zhu、Liangcai Gao Paper: https://arxiv.org/abs/2408.08623 Introduction Sketching is a fundamental artistic technique that captures the essence of real-world objects through lines and contours. Despite their simplicity, sketches can convey significant visual information, making them recognizable to humans. Recent advancements in deep learning have led to the development of automated sketch synthesis methods, which can save time and reduce costs compared to manual sketching. However, evaluating the quality of synthesized sketches remains a challenge due to the lack of a unified benchmark dataset and appropriate evaluation metrics. SketchRef Benchmark Dataset To address the limitations in current sketch…

Authors: João Gonçalves、Nick Jelicic、Michele Murgia、Evert Stamhuis Paper: https://arxiv.org/abs/2408.06931 The Advantages of Context-Specific Language Models: The Case of the Erasmian Language Model Introduction Large Language Models (LLMs) have become a focal point in machine learning, with significant investments aimed at enhancing their performance. The prevailing trend involves scaling up the number of parameters and the volume of training data. However, this approach incurs substantial computational, financial, and environmental costs, and raises privacy concerns. This paper introduces the Erasmian Language Model (ELM), a context-specific, 900-million parameter model developed for Erasmus University Rotterdam. The ELM demonstrates that smaller, context-specific models can perform adequately…