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pro vyhledávání: '"Li, Chengming"'
Autor:
Li, Zixuan, Xiong, Jing, Ye, Fanghua, Zheng, Chuanyang, Wu, Xun, Lu, Jianqiao, Wan, Zhongwei, Liang, Xiaodan, Li, Chengming, Sun, Zhenan, Kong, Lingpeng, Wong, Ngai
We present UncertaintyRAG, a novel approach for long-context Retrieval-Augmented Generation (RAG) that utilizes Signal-to-Noise Ratio (SNR)-based span uncertainty to estimate similarity between text chunks. This span uncertainty enhances model calibr
Externí odkaz:
http://arxiv.org/abs/2410.02719
Autor:
Luo, Jing, Luo, Run, Chen, Longze, Zhu, Liang, Ao, Chang, Li, Jiaming, Chen, Yukun, Cheng, Xin, Yang, Wen, Su, Jiayuan, Li, Chengming, Yang, Min
While closed-source Large Language Models (LLMs) demonstrate strong mathematical problem-solving abilities, open-source models continue to struggle with such tasks. To bridge this gap, we propose a data augmentation approach and introduce PersonaMath
Externí odkaz:
http://arxiv.org/abs/2410.01504
With the rapid development of deep learning methods, there have been many breakthroughs in the field of text classification. Models developed for this task have been shown to achieve high accuracy. However, most of these models are trained using labe
Externí odkaz:
http://arxiv.org/abs/2409.13787
The success of Large Language Models (LLMs) relies heavily on the huge amount of pre-training data learned in the pre-training phase. The opacity of the pre-training process and the training data causes the results of many benchmark tests to become u
Externí odkaz:
http://arxiv.org/abs/2409.01790
Since the invention of GPT2--1.5B in 2019, large language models (LLMs) have transitioned from specialized models to versatile foundation models. The LLMs exhibit impressive zero-shot ability, however, require fine-tuning on local datasets and signif
Externí odkaz:
http://arxiv.org/abs/2408.10691
Large Language Models (LLMs) have demonstrated exceptional performance across various natural language processing tasks, yet they occasionally tend to yield content that factually inaccurate or discordant with the expected output, a phenomenon empiri
Externí odkaz:
http://arxiv.org/abs/2408.08769
Autor:
Chen, Guhong, Fan, Liyang, Gong, Zihan, Xie, Nan, Li, Zixuan, Liu, Ziqiang, Li, Chengming, Qu, Qiang, Ni, Shiwen, Yang, Min
In this paper, we present a simulation system called AgentCourt that simulates the entire courtroom process. The judge, plaintiff's lawyer, defense lawyer, and other participants are autonomous agents driven by large language models (LLMs). Our core
Externí odkaz:
http://arxiv.org/abs/2408.08089
Autor:
Hu, Yuxuan, Tan, Minghuan, Zhang, Chenwei, Li, Zixuan, Liang, Xiaodan, Yang, Min, Li, Chengming, Hu, Xiping
Empathetic response generation is designed to comprehend the emotions of others and select the most appropriate strategies to assist them in resolving emotional challenges. Empathy can be categorized into cognitive empathy and affective empathy. The
Externí odkaz:
http://arxiv.org/abs/2407.21048
Autor:
Liang, Feng, Zhang, Zhen, Lu, Haifeng, Li, Chengming, Leung, Victor C. M., Guo, Yanyi, Hu, Xiping
With rapidly increasing distributed deep learning workloads in large-scale data centers, efficient distributed deep learning framework strategies for resource allocation and workload scheduling have become the key to high-performance deep learning. T
Externí odkaz:
http://arxiv.org/abs/2406.08115
Autor:
Liu, Ziqiang, Fang, Feiteng, Feng, Xi, Du, Xinrun, Zhang, Chenhao, Wang, Zekun, Bai, Yuelin, Zhao, Qixuan, Fan, Liyang, Gan, Chengguang, Lin, Hongquan, Li, Jiaming, Ni, Yuansheng, Wu, Haihong, Narsupalli, Yaswanth, Zheng, Zhigang, Li, Chengming, Hu, Xiping, Xu, Ruifeng, Chen, Xiaojun, Yang, Min, Liu, Jiaheng, Liu, Ruibo, Huang, Wenhao, Zhang, Ge, Ni, Shiwen
The rapid advancements in the development of multimodal large language models (MLLMs) have consistently led to new breakthroughs on various benchmarks. In response, numerous challenging and comprehensive benchmarks have been proposed to more accurate
Externí odkaz:
http://arxiv.org/abs/2406.05862