Zobrazeno 1 - 10
of 316
pro vyhledávání: '"MA Xiaoxing"'
Autor:
Li, Zenan, Zhou, Zhi, Yao, Yuan, Li, Yu-Feng, Cao, Chun, Yang, Fan, Zhang, Xian, Ma, Xiaoxing
A critical question about Large Language Models (LLMs) is whether their apparent deficiency in mathematical reasoning is inherent, or merely a result of insufficient exposure to high-quality mathematical data. To explore this, we developed an automat
Externí odkaz:
http://arxiv.org/abs/2412.04857
Autor:
Li, Zenan, Huang, Yunpeng, Li, Zhaoyu, Yao, Yuan, Xu, Jingwei, Chen, Taolue, Ma, Xiaoxing, Lu, Jian
Neuro-symbolic systems combine the abilities of neural perception and logical reasoning. However, end-to-end learning of neuro-symbolic systems is still an unsolved challenge. This paper proposes a natural framework that fuses neural network training
Externí odkaz:
http://arxiv.org/abs/2410.20957
Autoformalization, the task of automatically translating natural language descriptions into a formal language, poses a significant challenge across various domains, especially in mathematics. Recent advancements in large language models (LLMs) have u
Externí odkaz:
http://arxiv.org/abs/2410.20936
Autor:
Gao, Hao, Wang, Jingyue, Fang, Wenyang, Xu, Jingwei, Huang, Yunpeng, Chen, Taolue, Ma, Xiaoxing
Autonomous Driving Systems (ADS) require diverse and safety-critical traffic scenarios for effective training and testing, but the existing data generation methods struggle to provide flexibility and scalability. We propose LASER, a novel frame-work
Externí odkaz:
http://arxiv.org/abs/2410.16197
Autor:
Sun, Kexin, Ren, Yiding, Kuang, Hongyu, Gao, Hui, Ma, Xiaoxing, Rong, Guoping, Shao, Dong, Zhang, He
Traceability plays a vital role in facilitating various software development activities by establishing the traces between different types of artifacts (e.g., issues and commits in software repositories). Among the explorations for automated traceabi
Externí odkaz:
http://arxiv.org/abs/2409.19304
Autor:
Ouyang, Lingzhi, Sun, Xudong, Tang, Ruize, Huang, Yu, Jivrajani, Madhav, Ma, Xiaoxing, Xu, Tianyin
This paper presents our experience specifying and verifying the correctness of ZooKeeper, a complex and evolving distributed coordination system. We use TLA+ to model fine-grained behaviors of ZooKeeper and use the TLC model checker to verify its cor
Externí odkaz:
http://arxiv.org/abs/2409.14301
Recent studies in neuro-symbolic learning have explored the integration of logical knowledge into deep learning via encoding logical constraints as an additional loss function. However, existing approaches tend to vacuously satisfy logical constraint
Externí odkaz:
http://arxiv.org/abs/2403.00329
Neuro-symbolic learning generally consists of two separated worlds, i.e., neural network training and symbolic constraint solving, whose success hinges on symbol grounding, a fundamental problem in AI. This paper presents a novel, softened symbol gro
Externí odkaz:
http://arxiv.org/abs/2403.00323
As foundation models (FMs) continue to shape the landscape of AI, the in-context learning (ICL) paradigm thrives but also encounters issues such as toxicity, hallucination, disparity, adversarial vulnerability, and inconsistency. Ensuring the reliabi
Externí odkaz:
http://arxiv.org/abs/2402.17671
Automatic program repair (APR) techniques have the potential to reduce manual efforts in uncovering and repairing program defects during the code review (CR) process. However, the limited accuracy and considerable time costs associated with existing
Externí odkaz:
http://arxiv.org/abs/2312.17485