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pro vyhledávání: '"Verifier"'
Autor:
Guan, Xinyan, Liu, Yanjiang, Lu, Xinyu, Cao, Boxi, He, Ben, Han, Xianpei, Sun, Le, Lou, Jie, Yu, Bowen, Lu, Yaojie, Lin, Hongyu
The evolution of machine learning has increasingly prioritized the development of powerful models and more scalable supervision signals. However, the emergence of foundation models presents significant challenges in providing effective supervision si
Externí odkaz:
http://arxiv.org/abs/2411.11504
Implementing many important sub-circuits on near-term quantum devices remains a challenge due to the high levels of noise and the prohibitive depth on standard nearest-neighbour topologies. Overcoming these barriers will likely require quantum error
Externí odkaz:
http://arxiv.org/abs/2411.03245
Autor:
Pernsteiner, Stuart, Diatchki, Iavor S., Dockins, Robert, Dodds, Mike, Hendrix, Joe, Ravich, Tristan, Redmond, Patrick, Scott, Ryan, Tomb, Aaron
We present Crux, a cross-language verification tool for Rust and C/LLVM. Crux targets bounded, intricate pieces of code that are difficult for humans to get right: for example, cryptographic modules and serializer / deserializer pairs. Crux builds on
Externí odkaz:
http://arxiv.org/abs/2410.18280
One way to increase confidence in the outputs of Large Language Models (LLMs) is to support them with reasoning that is clear and easy to check -- a property we call legibility. We study legibility in the context of solving grade-school math problems
Externí odkaz:
http://arxiv.org/abs/2407.13692
Autor:
Milbrath, Jordan, Straub, Jeremy
The software for operations and network attack results review (SONARR) and the autonomous penetration testing system (APTS) use facts and common properties in digital twin networks to represent real-world entities. However, in some cases fact values
Externí odkaz:
http://arxiv.org/abs/2409.09174
Autor:
Gao, Bofei, Cai, Zefan, Xu, Runxin, Wang, Peiyi, Zheng, Ce, Lin, Runji, Lu, Keming, Liu, Dayiheng, Zhou, Chang, Xiao, Wen, Hu, Junjie, Liu, Tianyu, Chang, Baobao
In recent progress, mathematical verifiers have achieved success in mathematical reasoning tasks by validating the correctness of solutions generated by policy models. However, existing verifiers are trained with binary classification labels, which a
Externí odkaz:
http://arxiv.org/abs/2406.14024
Publikováno v:
Neural Computing and Applications, Volume 36, pages 2411 to 2427 (2024)
Signature verification is a critical task in many applications, including forensic science, legal judgments, and financial markets. However, current signature verification systems are often difficult to explain, which can limit their acceptance in th
Externí odkaz:
http://arxiv.org/abs/2405.12695
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Autor:
Lee, Jaeyoung, Lu, Ximing, Hessel, Jack, Brahman, Faeze, Yu, Youngjae, Bisk, Yonatan, Choi, Yejin, Gabriel, Saadia
Given the growing influx of misinformation across news and social media, there is a critical need for systems that can provide effective real-time verification of news claims. Large language or multimodal model based verification has been proposed to
Externí odkaz:
http://arxiv.org/abs/2407.00369
Autor:
Lu, Jianqiao, Dou, Zhiyang, Wang, Hongru, Cao, Zeyu, Dai, Jianbo, Wan, Yingjia, Guo, Zhijiang
In this work, we propose a novel method named \textbf{Auto}mated \textbf{P}rocess-\textbf{S}upervised \textbf{V}erifier (\textbf{\textsc{AutoPSV}}) to enhance the reasoning capabilities of large language models (LLMs) by automatically annotating the
Externí odkaz:
http://arxiv.org/abs/2405.16802