Zobrazeno 1 - 10
of 11 507
pro vyhledávání: '"Wang, Quan"'
Autor:
Ma, Shixuan, Wang, Quan
The increasing capability and widespread usage of large language models (LLMs) highlight the desirability of automatic detection of LLM-generated text. Zero-shot detectors, due to their training-free nature, have received considerable attention and n
Externí odkaz:
http://arxiv.org/abs/2409.16914
Autor:
Park, Hyun Jin, Agarwal, Dhruuv, Chen, Neng, Sun, Rentao, Partridge, Kurt, Chen, Justin, Zhang, Harry, Zhu, Pai, Bartel, Jacob, Kastner, Kyle, Wang, Gary, Rosenberg, Andrew, Wang, Quan
The keyword spotting (KWS) problem requires large amounts of real speech training data to achieve high accuracy across diverse populations. Utilizing large amounts of text-to-speech (TTS) synthesized data can reduce the cost and time associated with
Externí odkaz:
http://arxiv.org/abs/2408.10463
Large language models (LLMs) require model editing to efficiently update specific knowledge within them and avoid factual errors. Most model editing methods are solely designed for single-time use and lead to a significant forgetting effect after seq
Externí odkaz:
http://arxiv.org/abs/2408.11869
Autor:
Park, Hyun Jin, Agarwal, Dhruuv, Chen, Neng, Sun, Rentao, Partridge, Kurt, Chen, Justin, Zhang, Harry, Zhu, Pai, Bartel, Jacob, Kastner, Kyle, Wang, Gary, Rosenberg, Andrew, Wang, Quan
This paper explores the use of TTS synthesized training data for KWS (keyword spotting) task while minimizing development cost and time. Keyword spotting models require a huge amount of training data to be accurate, and obtaining such training data c
Externí odkaz:
http://arxiv.org/abs/2407.18879
One of the challenges in developing a high quality custom keyword spotting (KWS) model is the lengthy and expensive process of collecting training data covering a wide range of languages, phrases and speaking styles. We introduce Synth4Kws - a framew
Externí odkaz:
http://arxiv.org/abs/2407.16840
Autor:
Chi, Hanbin, Hu, Yueqiang, Ou, Xiangnian, Jiang, Yuting, Yu, Dian, Lou, Shaozhen, Wang, Quan, Xie, Qiong, Qiu, Cheng-Wei, Duan, Huigao
Flexible control light field across multiple parameters is the cornerstone of versatile and miniaturized optical devices. Metasurfaces, comprising subwavelength scatterers, offer a potent platform for executing such precise manipulations. However, th
Externí odkaz:
http://arxiv.org/abs/2407.00559
Autor:
Niu, Zhenxing, Sun, Yuyao, Ren, Haodong, Ji, Haoxuan, Wang, Quan, Ma, Xiaoke, Hua, Gang, Jin, Rong
This paper focuses on jailbreaking attacks against large language models (LLMs), eliciting them to generate objectionable content in response to harmful user queries. Unlike previous LLM-jailbreaks that directly orient to LLMs, our approach begins by
Externí odkaz:
http://arxiv.org/abs/2405.20015
Considered herein is the global existence of weak, strong solutions and Rayleigh-Taylor (RT) instability for 2D semi-dissipative Boussinesq equations in an infinite strip domain $\Omega_{\infty}$ subject to Navier boundary conditions with non-positiv
Externí odkaz:
http://arxiv.org/abs/2405.16074
Autor:
Wang, Quan, Pan, Mingliang, Kreiss, Lucas, Samaei, Saeed, Carp, Stefan A., Johansson, Johannes D., Zhang, Yuanzhe, Wu, Melissa, Horstmeyer, Roarke, Diop, Mamadou, Li, David Day-Uei
Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might
Externí odkaz:
http://arxiv.org/abs/2406.15420
In-context learning (ICL), which promotes inference with several demonstrations, has become a widespread paradigm to stimulate LLM capabilities for downstream tasks. Due to context length constraints, it cannot be further improved in spite of more tr
Externí odkaz:
http://arxiv.org/abs/2405.10738