Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Gao, Bofei"'
Autor:
Yang, An, Zhang, Beichen, Hui, Binyuan, Gao, Bofei, Yu, Bowen, Li, Chengpeng, Liu, Dayiheng, Tu, Jianhong, Zhou, Jingren, Lin, Junyang, Lu, Keming, Xue, Mingfeng, Lin, Runji, Liu, Tianyu, Ren, Xingzhang, Zhang, Zhenru
In this report, we present a series of math-specific large language models: Qwen2.5-Math and Qwen2.5-Math-Instruct-1.5B/7B/72B. The core innovation of the Qwen2.5 series lies in integrating the philosophy of self-improvement throughout the entire pip
Externí odkaz:
http://arxiv.org/abs/2409.12122
Autor:
Gao, Bofei, Song, Feifan, Miao, Yibo, Cai, Zefan, Yang, Zhe, Chen, Liang, Hu, Helan, Xu, Runxin, Dong, Qingxiu, Zheng, Ce, Xiao, Wen, Zhang, Ge, Zan, Daoguang, Lu, Keming, Yu, Bowen, Liu, Dayiheng, Cui, Zeyu, Yang, Jian, Sha, Lei, Wang, Houfeng, Sui, Zhifang, Wang, Peiyi, Liu, Tianyu, Chang, Baobao
Large Language Models (LLMs) exhibit remarkably powerful capabilities. One of the crucial factors to achieve success is aligning the LLM's output with human preferences. This alignment process often requires only a small amount of data to efficiently
Externí odkaz:
http://arxiv.org/abs/2409.02795
Autor:
Gao, Bofei, Cai, Zefan, Xu, Runxin, Wang, Peiyi, Zheng, Ce, Lin, Runji, Lu, Keming, Liu, Dayiheng, Zhou, Chang, Xiao, Wen, Hu, Junjie, Liu, Tianyu, Chang, Baobao
Mathematical verfier achieves success in mathematical reasoning tasks by validating the correctness of solutions. However, existing verifiers are trained with binary classification labels, which are not informative enough for the model to accurately
Externí odkaz:
http://arxiv.org/abs/2406.14024
Autor:
Cai, Zefan, Zhang, Yichi, Gao, Bofei, Liu, Yuliang, Liu, Tianyu, Lu, Keming, Xiong, Wayne, Dong, Yue, Chang, Baobao, Hu, Junjie, Xiao, Wen
In this study, we investigate whether attention-based information flow inside large language models (LLMs) is aggregated through noticeable patterns for long context processing. Our observations reveal that LLMs aggregate information through Pyramida
Externí odkaz:
http://arxiv.org/abs/2406.02069
Frame identification aims to find semantic frames associated with target words in a sentence. Recent researches measure the similarity or matching score between targets and candidate frames by modeling frame definitions. However, they either lack suf
Externí odkaz:
http://arxiv.org/abs/2310.13316
Abstract Meaning Representation (AMR) parsing aims to extract an abstract semantic graph from a given sentence. The sequence-to-sequence approaches, which linearize the semantic graph into a sequence of nodes and edges and generate the linearized gra
Externí odkaz:
http://arxiv.org/abs/2310.08860
In this paper, we provide a detailed description of our system at CAMRP-2022 evaluation. We firstly propose a two-stage method to conduct Chinese AMR Parsing with alignment generation, which includes Concept-Prediction and Relation-Prediction stages.
Externí odkaz:
http://arxiv.org/abs/2209.14512