Zobrazeno 1 - 10
of 43 667
pro vyhledávání: '"WANG, Lu"'
Reliable responses of service chatbots are often achieved by employing retrieval-based methods that restrict answers to a knowledge base comprising predefined question-answer pairs (QA pairs). To accommodate potential variations in how a customer's q
Externí odkaz:
http://arxiv.org/abs/2410.12444
Providing feedback is widely recognized as crucial for refining students' writing skills. Recent advances in language models (LMs) have made it possible to automatically generate feedback that is actionable and well-aligned with human-specified attri
Externí odkaz:
http://arxiv.org/abs/2410.08058
Narrative-of-Thought: Improving Temporal Reasoning of Large Language Models via Recounted Narratives
Reasoning about time and temporal relations is an integral aspect of human cognition, essential for perceiving the world and navigating our experiences. Though large language models (LLMs) have demonstrated impressive performance in many reasoning ta
Externí odkaz:
http://arxiv.org/abs/2410.05558
We study the problem of fine-tuning a language model (LM) for a target task by optimally using the information from $n$ auxiliary tasks. This problem has broad applications in NLP, such as targeted instruction tuning and data selection in chain-of-th
Externí odkaz:
http://arxiv.org/abs/2409.19458
Autor:
Lu, Junting, Zhang, Zhiyang, Yang, Fangkai, Zhang, Jue, Wang, Lu, Du, Chao, Lin, Qingwei, Rajmohan, Saravan, Zhang, Dongmei, Zhang, Qi
Multimodal large language models (MLLMs) have enabled LLM-based agents to directly interact with application user interfaces (UIs), enhancing agents' performance in complex tasks. However, these agents often suffer from high latency and low reliabili
Externí odkaz:
http://arxiv.org/abs/2409.17140
With recent breakthroughs in deep neural networks, numerous tasks within autonomous driving have exhibited remarkable performance. However, deep learning models are susceptible to adversarial attacks, presenting significant security risks to autonomo
Externí odkaz:
http://arxiv.org/abs/2409.07706
Autor:
Zhang, Tianyuan, Wang, Lu, Kang, Jiaqi, Zhang, Xinwei, Liang, Siyuan, Chen, Yuwei, Liu, Aishan, Liu, Xianglong
Recent advances in deep learning have markedly improved autonomous driving (AD) models, particularly end-to-end systems that integrate perception, prediction, and planning stages, achieving state-of-the-art performance. However, these models remain v
Externí odkaz:
http://arxiv.org/abs/2409.07321
We find that the cross-entropy loss curves of neural language models empirically adhere to a scaling law with learning rate (LR) annealing over training steps ($s$): $$L(s) = L_0 + A\cdot S_1^{-\alpha} - C\cdot S_2$$ Where $S_1$ is forward area and $
Externí odkaz:
http://arxiv.org/abs/2408.11029
Image restoration endeavors to reconstruct a high-quality, detail-rich image from a degraded counterpart, which is a pivotal process in photography and various computer vision systems. In real-world scenarios, different types of degradation can cause
Externí odkaz:
http://arxiv.org/abs/2408.10145
Diverse critical data, such as location information and driving patterns, can be collected by IoT devices in vehicular networks to improve driving experiences and road safety. However, drivers are often reluctant to share their data due to privacy co
Externí odkaz:
http://arxiv.org/abs/2408.03446