Zobrazeno 1 - 10
of 494
pro vyhledávání: '"Renze, P."'
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:
Yang, Zebin, Chen, Renze, Wu, Taiqiang, Wong, Ngai, Liang, Yun, Wang, Runsheng, Huang, Ru, Li, Meng
In this paper, we propose MCUBERT to enable language models like BERT on tiny microcontroller units (MCUs) through network and scheduling co-optimization. We observe the embedding table contributes to the major storage bottleneck for tiny BERT models
Externí odkaz:
http://arxiv.org/abs/2410.17957
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
Large Language Models (LLMs), especially those accessed via APIs, have demonstrated impressive capabilities across various domains. However, users without technical expertise often turn to (untrustworthy) third-party services, such as prompt engineer
Externí odkaz:
http://arxiv.org/abs/2406.05948
Autor:
Renze, Matthew, Guven, Erhan
In this study, we investigated the effects of self-reflection in large language models (LLMs) on problem-solving performance. We instructed nine popular LLMs to answer a series of multiple-choice questions to provide a performance baseline. For each
Externí odkaz:
http://arxiv.org/abs/2405.06682
IoT devices based on microcontroller units (MCU) provide ultra-low power consumption and ubiquitous computation for near-sensor deep learning models (DNN). However, the memory of MCU is usually 2-3 orders of magnitude smaller than mobile devices, whi
Externí odkaz:
http://arxiv.org/abs/2406.06542
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:
Renze, Matthew, Guven, Erhan
In this research study, we empirically investigate the effect of sampling temperature on the performance of Large Language Models (LLMs) on various problem-solving tasks. We created a multiple-choice question-and-answer (MCQA) exam by randomly sampli
Externí odkaz:
http://arxiv.org/abs/2402.05201