Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Dong, Guanting"'
Autor:
Yang, An, Yang, Baosong, Hui, Binyuan, Zheng, Bo, Yu, Bowen, Zhou, Chang, Li, Chengpeng, Li, Chengyuan, Liu, Dayiheng, Huang, Fei, Dong, Guanting, Wei, Haoran, Lin, Huan, Tang, Jialong, Wang, Jialin, Yang, Jian, Tu, Jianhong, Zhang, Jianwei, Ma, Jianxin, Yang, Jianxin, Xu, Jin, Zhou, Jingren, Bai, Jinze, He, Jinzheng, Lin, Junyang, Dang, Kai, Lu, Keming, Chen, Keqin, Yang, Kexin, Li, Mei, Xue, Mingfeng, Ni, Na, Zhang, Pei, Wang, Peng, Peng, Ru, Men, Rui, Gao, Ruize, Lin, Runji, Wang, Shijie, Bai, Shuai, Tan, Sinan, Zhu, Tianhang, Li, Tianhao, Liu, Tianyu, Ge, Wenbin, Deng, Xiaodong, Zhou, Xiaohuan, Ren, Xingzhang, Zhang, Xinyu, Wei, Xipin, Ren, Xuancheng, Liu, Xuejing, Fan, Yang, Yao, Yang, Zhang, Yichang, Wan, Yu, Chu, Yunfei, Liu, Yuqiong, Cui, Zeyu, Zhang, Zhenru, Guo, Zhifang, Fan, Zhihao
This report introduces the Qwen2 series, the latest addition to our large language models and large multimodal models. We release a comprehensive suite of foundational and instruction-tuned language models, encompassing a parameter range from 0.5 to
Externí odkaz:
http://arxiv.org/abs/2407.10671
Large language models (LLMs) have made impressive progress in handling simple math problems, yet they still struggle with more challenging and complex mathematical tasks. In this paper, we introduce a series of LLMs that employs the Decomposition of
Externí odkaz:
http://arxiv.org/abs/2407.04078
Autor:
Qiao, Runqi, Tan, Qiuna, Dong, Guanting, Wu, Minhui, Sun, Chong, Song, Xiaoshuai, GongQue, Zhuoma, Lei, Shanglin, Wei, Zhe, Zhang, Miaoxuan, Qiao, Runfeng, Zhang, Yifan, Zong, Xiao, Xu, Yida, Diao, Muxi, Bao, Zhimin, Li, Chen, Zhang, Honggang
Visual mathematical reasoning, as a fundamental visual reasoning ability, has received widespread attention from the Large Multimodal Models (LMMs) community. Existing benchmarks, such as MathVista and MathVerse, focus more on the result-oriented per
Externí odkaz:
http://arxiv.org/abs/2407.01284
Retrieval-augmented generation (RAG) has demonstrated effectiveness in mitigating the hallucination problem of large language models (LLMs). However, the difficulty of aligning the retriever with the diverse LLMs' knowledge preferences inevitably pos
Externí odkaz:
http://arxiv.org/abs/2406.18676
Autor:
Dong, Guanting, Lu, Keming, Li, Chengpeng, Xia, Tingyu, Yu, Bowen, Zhou, Chang, Zhou, Jingren
One core capability of large language models (LLMs) is to follow natural language instructions. However, the issue of automatically constructing high-quality training data to enhance the complex instruction-following abilities of LLMs without manual
Externí odkaz:
http://arxiv.org/abs/2406.13542
Autor:
Song, Xiaoshuai, Diao, Muxi, Dong, Guanting, Wang, Zhengyang, Fu, Yujia, Qiao, Runqi, Wang, Zhexu, Fu, Dayuan, Wu, Huangxuan, Liang, Bin, Zeng, Weihao, Wang, Yejie, GongQue, Zhuoma, Yu, Jianing, Tan, Qiuna, Xu, Weiran
Computer Science (CS) stands as a testament to the intricacies of human intelligence, profoundly advancing the development of artificial intelligence and modern society. However, the current community of large language models (LLMs) overly focuses on
Externí odkaz:
http://arxiv.org/abs/2406.08587
Autor:
Zhao, Jinxu, Dong, Guanting, Qiu, Yueyan, Hui, Tingfeng, Song, Xiaoshuai, Guo, Daichi, Xu, Weiran
In a realistic dialogue system, the input information from users is often subject to various types of input perturbations, which affects the slot-filling task. Although rule-based data augmentation methods have achieved satisfactory results, they fai
Externí odkaz:
http://arxiv.org/abs/2402.14494
Addressing the discrepancies between predictions and actual outcomes often aids individuals in expanding their thought processes and engaging in reflection, thereby facilitating reasoning in the correct direction. In this paper, we introduce $\textbf
Externí odkaz:
http://arxiv.org/abs/2402.11534
In the deployment of large language models (LLMs), accurate confidence estimation is critical for assessing the credibility of model predictions. However, existing methods often fail to overcome the issue of overconfidence on incorrect answers. In th
Externí odkaz:
http://arxiv.org/abs/2402.11279
Autor:
Wang, Yejie, He, Keqing, Dong, Guanting, Wang, Pei, Zeng, Weihao, Diao, Muxi, Mou, Yutao, Zhang, Mengdi, Wang, Jingang, Cai, Xunliang, Xu, Weiran
Code Large Language Models (Code LLMs) have demonstrated outstanding performance in code-related tasks. Several instruction tuning approaches have been proposed to boost the code generation performance of pre-trained Code LLMs. In this paper, we intr
Externí odkaz:
http://arxiv.org/abs/2402.09136