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This paper presents the T031 team's approach to the StutteringSpeech Challenge in SLT2024. Mandarin Stuttering Event Detection (MSED) aims to detect instances of stuttering events in Mandarin speech. We propose a detailed acoustic analysis method to
Externí odkaz:
http://arxiv.org/abs/2410.05647
Unsupervised rationale extraction aims to extract text snippets to support model predictions without explicit rationale annotation. Researchers have made many efforts to solve this task. Previous works often encode each aspect independently, which ma
Externí odkaz:
http://arxiv.org/abs/2410.03531
TREB, a novel tabular imputation framework utilizing BERT, introduces a groundbreaking approach for handling missing values in tabular data. Unlike traditional methods that often overlook the specific demands of imputation, TREB leverages the robust
Externí odkaz:
http://arxiv.org/abs/2410.00022
Warning: this paper contains model outputs exhibiting unethical information. Large Language Models (LLMs) have achieved significant breakthroughs, but their generated unethical content poses potential risks. Measuring value alignment of LLMs becomes
Externí odkaz:
http://arxiv.org/abs/2406.14230
Understanding the evolution of 3D scenes is important for effective autonomous driving. While conventional methods mode scene development with the motion of individual instances, world models emerge as a generative framework to describe the general s
Externí odkaz:
http://arxiv.org/abs/2405.20337
Despite the remarkable achievements of language models (LMs) across a broad spectrum of tasks, their propensity for generating toxic outputs remains a prevalent concern. Current solutions involving finetuning or auxiliary models usually require exten
Externí odkaz:
http://arxiv.org/abs/2404.10464
Autor:
Yang, Shu, Su, Jiayuan, Jiang, Han, Li, Mengdi, Cheng, Keyuan, Ali, Muhammad Asif, Hu, Lijie, Wang, Di
With the rise of large language models (LLMs), ensuring they embody the principles of being helpful, honest, and harmless (3H), known as Human Alignment, becomes crucial. While existing alignment methods like RLHF, DPO, etc., effectively fine-tune LL
Externí odkaz:
http://arxiv.org/abs/2404.00486
Current Neural Radiance Fields (NeRF) can generate photorealistic novel views. For editing 3D scenes represented by NeRF, with the advent of generative models, this paper proposes Inpaint4DNeRF to capitalize on state-of-the-art stable diffusion model
Externí odkaz:
http://arxiv.org/abs/2401.00208
Autor:
Wang, Lening, Ren, Yilong, Jiang, Han, Cai, Pinlong, Fu, Daocheng, Wang, Tianqi, Cui, Zhiyong, Yu, Haiyang, Wang, Xuesong, Zhou, Hanchu, Huang, Helai, Wang, Yinhai
Traffic accidents, being a significant contributor to both human casualties and property damage, have long been a focal point of research for many scholars in the field of traffic safety. However, previous studies, whether focusing on static environm
Externí odkaz:
http://arxiv.org/abs/2312.13156