Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Lou, Renze"'
Autor:
Lou, Renze, Xu, Hanzi, Wang, Sijia, Du, Jiangshu, Kamoi, Ryo, Lu, Xiaoxin, Xie, Jian, Sun, Yuxuan, Zhang, Yusen, Ahn, Jihyun Janice, Fang, Hongchao, Zou, Zhuoyang, Ma, Wenchao, Li, Xi, Zhang, Kai, Xia, Congying, Huang, Lifu, Yin, Wenpeng
Numerous studies have assessed the proficiency of AI systems, particularly large language models (LLMs), in facilitating everyday tasks such as email writing, question answering, and creative content generation. However, researchers face unique chall
Externí odkaz:
http://arxiv.org/abs/2410.22394
Autor:
Zhang, Kexun, Yao, Weiran, Liu, Zuxin, Feng, Yihao, Liu, Zhiwei, Murthy, Rithesh, Lan, Tian, Li, Lei, Lou, Renze, Xu, Jiacheng, Pang, Bo, Zhou, Yingbo, Heinecke, Shelby, Savarese, Silvio, Wang, Huan, Xiong, Caiming
Large language model (LLM) agents have shown great potential in solving real-world software engineering (SWE) problems. The most advanced open-source SWE agent can resolve over 27% of real GitHub issues in SWE-Bench Lite. However, these sophisticated
Externí odkaz:
http://arxiv.org/abs/2408.07060
Autor:
Du, Jiangshu, Wang, Yibo, Zhao, Wenting, Deng, Zhongfen, Liu, Shuaiqi, Lou, Renze, Zou, Henry Peng, Venkit, Pranav Narayanan, Zhang, Nan, Srinath, Mukund, Zhang, Haoran Ranran, Gupta, Vipul, Li, Yinghui, Li, Tao, Wang, Fei, Liu, Qin, Liu, Tianlin, Gao, Pengzhi, Xia, Congying, Xing, Chen, Cheng, Jiayang, Wang, Zhaowei, Su, Ying, Shah, Raj Sanjay, Guo, Ruohao, Gu, Jing, Li, Haoran, Wei, Kangda, Wang, Zihao, Cheng, Lu, Ranathunga, Surangika, Fang, Meng, Fu, Jie, Liu, Fei, Huang, Ruihong, Blanco, Eduardo, Cao, Yixin, Zhang, Rui, Yu, Philip S., Yin, Wenpeng
This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and question answering, significantly reducing the time required for many rout
Externí odkaz:
http://arxiv.org/abs/2406.16253
Autor:
Xu, Hanzi, Lou, Renze, Du, Jiangshu, Mahzoon, Vahid, Talebianaraki, Elmira, Zhou, Zhuoan, Garrison, Elizabeth, Vucetic, Slobodan, Yin, Wenpeng
In many classification tasks designed for AI or human to solve, gold labels are typically included within the label space by default, often posed as "which of the following is correct?" This standard setup has traditionally highlighted the strong per
Externí odkaz:
http://arxiv.org/abs/2406.16203
Backdoor attacks present significant threats to Large Language Models (LLMs), particularly with the rise of third-party services that offer API integration and prompt engineering. Untrustworthy third parties can plant backdoors into LLMs and pose ris
Externí odkaz:
http://arxiv.org/abs/2406.05948
Autor:
Kamoi, Ryo, Das, Sarkar Snigdha Sarathi, Lou, Renze, Ahn, Jihyun Janice, Zhao, Yilun, Lu, Xiaoxin, Zhang, Nan, Zhang, Yusen, Zhang, Ranran Haoran, Vummanthala, Sujeeth Reddy, Dave, Salika, Qin, Shaobo, Cohan, Arman, Yin, Wenpeng, Zhang, Rui
With Large Language Models (LLMs) being widely used across various tasks, detecting errors in their responses is increasingly crucial. However, little research has been conducted on error detection of LLM responses. Collecting error annotations on LL
Externí odkaz:
http://arxiv.org/abs/2404.03602
Autor:
Xie, Jian, Zhang, Kai, Chen, Jiangjie, Zhu, Tinghui, Lou, Renze, Tian, Yuandong, Xiao, Yanghua, Su, Yu
Planning has been part of the core pursuit for artificial intelligence since its conception, but earlier AI agents mostly focused on constrained settings because many of the cognitive substrates necessary for human-level planning have been lacking. R
Externí odkaz:
http://arxiv.org/abs/2402.01622
Mathematical reasoning serves as a cornerstone for assessing the fundamental cognitive capabilities of human intelligence. In recent times, there has been a notable surge in the development of Large Language Models (LLMs) geared towards the automated
Externí odkaz:
http://arxiv.org/abs/2402.00157
Multimodal information extraction (MIE) gains significant attention as the popularity of multimedia content increases. However, current MIE methods often resort to using task-specific model structures, which results in limited generalizability across
Externí odkaz:
http://arxiv.org/abs/2401.03082
In the realm of large language models (LLMs), enhancing instruction-following capability often involves curating expansive training data. This is achieved through two primary schemes: i) Scaling-Inputs: Amplifying (input, output) pairs per task instr
Externí odkaz:
http://arxiv.org/abs/2312.02436