Search Results for author: Yanjun Shen

Found 8 papers, 5 papers with code

Baichuan Alignment Technical Report

no code implementations19 Oct 2024 MingAn Lin, Fan Yang, Yanjun Shen, Haoze Sun, Tianpeng Li, Chenzheng Zhu, Tao Zhang, Miao Zheng, Xu Li, Yijie Zhou, Mingyang Chen, Yanzhao Qin, Youquan Li, Hao Liang, Fei Li, Yadong Li, Mang Wang, Guosheng Dong, Kun Fang, Jianhua Xu, Bin Cui, Wentao Zhang, Zenan Zhou, WeiPeng Chen

Baichuan-Instruct is an internal model, while Qwen2-Nova-72B and Llama3-PBM-Nova-70B are instruct versions of the Qwen2-72B and Llama-3-70B base models, optimized through Baichuan Alignment.

Baichuan-Omni Technical Report

2 code implementations11 Oct 2024 Yadong Li, Haoze Sun, MingAn Lin, Tianpeng Li, Guosheng Dong, Bowen Ding, Wei Song, Zhenglin Cheng, Yuqi Huo, Song Chen, Xu Li, Da Pan, Shusen Zhang, Xin Wu, Zheng Liang, Jun Liu, Tao Zhang, Keer Lu, Yaqi Zhao, Yanjun Shen, Fan Yang, Kaicheng Yu, Tao Lin, Jianhua Xu, Zenan Zhou, WeiPeng Chen

The salient multimodal capabilities and interactive experience of GPT-4o highlight its critical role in practical applications, yet it lacks a high-performing open-source counterpart.

Language Modeling Language Modelling +3

SysBench: Can Large Language Models Follow System Messages?

1 code implementation20 Aug 2024 Yanzhao Qin, Tao Zhang, Yanjun Shen, Wenjing Luo, Haoze Sun, Yan Zhang, Yujing Qiao, WeiPeng Chen, Zenan Zhou, Wentao Zhang, Bin Cui

Finally, we conduct extensive evaluation across various existing LLMs, measuring their ability to follow specified constraints given in system messages.

CFBench: A Comprehensive Constraints-Following Benchmark for LLMs

1 code implementation2 Aug 2024 Yanjun Shen, Wenjing Luo, Yan Zhang, Hao Liang, Tao Zhang, Fan Yang, MingAn Lin, Yujing Qiao, WeiPeng Chen, Bin Cui, Wentao Zhang, Zenan Zhou

The adeptness of Large Language Models (LLMs) in comprehending and following natural language instructions is critical for their deployment in sophisticated real-world applications.

PAS: Data-Efficient Plug-and-Play Prompt Augmentation System

no code implementations8 Jul 2024 Miao Zheng, Hao Liang, Fan Yang, Haoze Sun, Tianpeng Li, Lingchu Xiong, Yan Zhang, Youzhen Wu, Kun Li, Yanjun Shen, MingAn Lin, Tao Zhang, Guosheng Dong, Yujing Qiao, Kun Fang, WeiPeng Chen, Bin Cui, Wentao Zhang, Zenan Zhou

This combination of high performance, efficiency, and flexibility makes PAS a valuable system for enhancing the usability and effectiveness of LLMs through improved prompt engineering.

Prompt Engineering

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