Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Autor: Abdin, Marah, Aneja, Jyoti, Awadalla, Hany, Awadallah, Ahmed, Awan, Ammar Ahmad, Bach, Nguyen, Bahree, Amit, Bakhtiari, Arash, Bao, Jianmin, Behl, Harkirat, Benhaim, Alon, Bilenko, Misha, Bjorck, Johan, Bubeck, Sébastien, Cai, Martin, Cai, Qin, Chaudhary, Vishrav, Chen, Dong, Chen, Dongdong, Chen, Weizhu, Chen, Yen-Chun, Chen, Yi-Ling, Cheng, Hao, Chopra, Parul, Dai, Xiyang, Dixon, Matthew, Eldan, Ronen, Fragoso, Victor, Gao, Jianfeng, Gao, Mei, Gao, Min, Garg, Amit, Del Giorno, Allie, Goswami, Abhishek, Gunasekar, Suriya, Haider, Emman, Hao, Junheng, Hewett, Russell J., Hu, Wenxiang, Huynh, Jamie, Iter, Dan, Jacobs, Sam Ade, Javaheripi, Mojan, Jin, Xin, Karampatziakis, Nikos, Kauffmann, Piero, Khademi, Mahoud, Kim, Dongwoo, Kim, Young Jin, Kurilenko, Lev, Lee, James R., Lee, Yin Tat, Li, Yuanzhi, Li, Yunsheng, Liang, Chen, Liden, Lars, Lin, Xihui, Lin, Zeqi, Liu, Ce, Liu, Liyuan, Liu, Mengchen, Liu, Weishung, Liu, Xiaodong, Luo, Chong, Madan, Piyush, Mahmoudzadeh, Ali, Majercak, David, Mazzola, Matt, Mendes, Caio César Teodoro, Mitra, Arindam, Modi, Hardik, Nguyen, Anh, Norick, Brandon, Patra, Barun, Perez-Becker, Daniel, Portet, Thomas, Pryzant, Reid, Qin, Heyang, Radmilac, Marko, Ren, Liliang, de Rosa, Gustavo, Rosset, Corby, Roy, Sambudha, Ruwase, Olatunji, Saarikivi, Olli, Saied, Amin, Salim, Adil, Santacroce, Michael, Shah, Shital, Shang, Ning, Sharma, Hiteshi, Shen, Yelong, Shukla, Swadheen, Song, Xia, Tanaka, Masahiro, Tupini, Andrea, Vaddamanu, Praneetha, Wang, Chunyu, Wang, Guanhua, Wang, Lijuan, Wang, Shuohang, Wang, Xin, Wang, Yu, Ward, Rachel, Wen, Wen, Witte, Philipp, Wu, Haiping, Wu, Xiaoxia, Wyatt, Michael, Xiao, Bin, Xu, Can, Xu, Jiahang, Xu, Weijian, Xue, Jilong, Yadav, Sonali, Yang, Fan, Yang, Jianwei, Yang, Yifan, Yang, Ziyi, Yu, Donghan, Yuan, Lu, Zhang, Chenruidong, Zhang, Cyril, Zhang, Jianwen, Zhang, Li Lyna, Zhang, Yi, Zhang, Yue, Zhang, Yunan, Zhou, Xiren

We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi

Externí odkaz: http://arxiv.org/abs/2404.14219

Differentially Private Synthetic Data via Foundation Model APIs 2: Text

Autor: Xie, Chulin, Lin, Zinan, Backurs, Arturs, Gopi, Sivakanth, Yu, Da, Inan, Huseyin A, Nori, Harsha, Jiang, Haotian, Zhang, Huishuai, Lee, Yin Tat, Li, Bo, Yekhanin, Sergey

Text data has become extremely valuable due to the emergence of machine learning algorithms that learn from it. A lot of high-quality text data generated in the real world is private and therefore cannot be shared or used freely due to privacy concer

Externí odkaz: http://arxiv.org/abs/2403.01749

Chapter Predictive Safety Monitoring for Lifting Operations with Vision-Based Crane-Worker Conflict Prediction

Autor: Lam, Chin Pok, Lee, Yin Ni, Ting, Chung Lam, Wong, Peter Kok-Yiu, Cheng, Jack C. P., Leung, Pak Him

Construction industry has reported among the highest accident and fatality rates over the past decade. In particular, crane lifting is a notably hazardous operation on construction sites, causing fatal accidents like workers being struck by the boom

Externí odkaz: https://library.oapen.org/handle/20.500.12657/89068

Cross-cultural adaptation and content validation of the Singapore English version of EQ-5D-Y: a qualitative study

Autor: Rachel Lee-Yin Tan, Zhi Min Ng, Le Ann Chen, Michael Herdman, Nan Luo

Publikováno v: Health and Quality of Life Outcomes, Vol 22, Iss 1, Pp 1-10 (2024)

Abstract Background The EQ-5D-Y is a generic preference-weighted measure for children and adolescents which was developed within Europe. Two versions exist, the EQ-5D-Y-3L (Y-3L) and EQ-5D-Y-5L (Y-5L). This study aimed to cross-culturally adapt the Y

Externí odkaz: https://doaj.org/article/b8c8608ce6e74f129478810553b9659c

MathChat: Converse to Tackle Challenging Math Problems with LLM Agents

Autor: Wu, Yiran, Jia, Feiran, Zhang, Shaokun, Li, Hangyu, Zhu, Erkang, Wang, Yue, Lee, Yin Tat, Peng, Richard, Wu, Qingyun, Wang, Chi

Employing Large Language Models (LLMs) to address mathematical problems is an intriguing research endeavor, considering the abundance of math problems expressed in natural language across numerous science and engineering fields. LLMs, with their gene

Externí odkaz: http://arxiv.org/abs/2306.01337