Zobrazeno 1 - 10
of 277
pro vyhledávání: '"Zhang, Tianhang"'
Autor:
Jiayang, Cheng, Chan, Chunkit, Zhuang, Qianqian, Qiu, Lin, Zhang, Tianhang, Liu, Tengxiao, Song, Yangqiu, Zhang, Yue, Liu, Pengfei, Zhang, Zheng
The rise of large language models (LLMs) has significantly influenced the quality of information in decision-making systems, leading to the prevalence of AI-generated content and challenges in detecting misinformation and managing conflicting informa
Externí odkaz:
http://arxiv.org/abs/2410.04068
Autor:
Ru, Dongyu, Qiu, Lin, Hu, Xiangkun, Zhang, Tianhang, Shi, Peng, Chang, Shuaichen, Jiayang, Cheng, Wang, Cunxiang, Sun, Shichao, Li, Huanyu, Zhang, Zizhao, Wang, Binjie, Jiang, Jiarong, He, Tong, Wang, Zhiguo, Liu, Pengfei, Zhang, Yue, Zhang, Zheng
Despite Retrieval-Augmented Generation (RAG) showing promising capability in leveraging external knowledge, a comprehensive evaluation of RAG systems is still challenging due to the modular nature of RAG, evaluation of long-form responses and reliabi
Externí odkaz:
http://arxiv.org/abs/2408.08067
This study targets Multi-Lighting Image Anomaly Detection (MLIAD), where multiple lighting conditions are utilized to enhance imaging quality and anomaly detection performance. While numerous image anomaly detection methods have been proposed, they l
Externí odkaz:
http://arxiv.org/abs/2406.04573
Autor:
Hu, Xiangkun, Ru, Dongyu, Qiu, Lin, Guo, Qipeng, Zhang, Tianhang, Xu, Yang, Luo, Yun, Liu, Pengfei, Zhang, Yue, Zhang, Zheng
Large Language Models (LLMs) have shown impressive capabilities but also a concerning tendency to hallucinate. This paper presents RefChecker, a framework that introduces claim-triplets to represent claims in LLM responses, aiming to detect fine-grai
Externí odkaz:
http://arxiv.org/abs/2405.14486
Autor:
Sheng, Shuqian, Xu, Yi, Zhang, Tianhang, Shen, Zanwei, Fu, Luoyi, Ding, Jiaxin, Zhou, Lei, Wang, Xinbing, Zhou, Chenghu
Automatic evaluation metrics for generated texts play an important role in the NLG field, especially with the rapid growth of LLMs. However, existing metrics are often limited to specific scenarios, making it challenging to meet the evaluation requir
Externí odkaz:
http://arxiv.org/abs/2404.19563
Large language models (LLMs) demonstrate great performance in text generation. However, LLMs are still suffering from hallucinations. In this work, we propose an inference-time method, Self-Highlighted Hesitation (SH2), to help LLMs decode more truth
Externí odkaz:
http://arxiv.org/abs/2401.05930
Autor:
Lin, Zhouhan, Deng, Cheng, Zhou, Le, Zhang, Tianhang, Xu, Yi, Xu, Yutong, He, Zhongmou, Shi, Yuanyuan, Dai, Beiya, Song, Yunchong, Zeng, Boyi, Chen, Qiyuan, Miao, Yuxun, Xue, Bo, Wang, Shu, Fu, Luoyi, Zhang, Weinan, He, Junxian, Zhu, Yunqiang, Wang, Xinbing, Zhou, Chenghu
Large language models (LLMs) have achieved huge success for their general knowledge and ability to solve a wide spectrum of tasks in natural language processing (NLP). Due to their impressive abilities, LLMs have shed light on potential inter-discipl
Externí odkaz:
http://arxiv.org/abs/2401.00434
Autor:
Zhang, Tianhang, Qiu, Lin, Guo, Qipeng, Deng, Cheng, Zhang, Yue, Zhang, Zheng, Zhou, Chenghu, Wang, Xinbing, Fu, Luoyi
Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields. However, LLMs are prone to hallucinate untruthful or nonsensical outputs that fail to meet user expectations in many real-world ap
Externí odkaz:
http://arxiv.org/abs/2311.13230
Autor:
Wang, Cunxiang, Liu, Xiaoze, Yue, Yuanhao, Tang, Xiangru, Zhang, Tianhang, Jiayang, Cheng, Yao, Yunzhi, Gao, Wenyang, Hu, Xuming, Qi, Zehan, Wang, Yidong, Yang, Linyi, Wang, Jindong, Xie, Xing, Zhang, Zheng, Zhang, Yue
This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital. We define the Factuality Issue as the probability of
Externí odkaz:
http://arxiv.org/abs/2310.07521
Autor:
Deng, Cheng, Zhang, Tianhang, He, Zhongmou, Xu, Yi, Chen, Qiyuan, Shi, Yuanyuan, Fu, Luoyi, Zhang, Weinan, Wang, Xinbing, Zhou, Chenghu, Lin, Zhouhan, He, Junxian
Large language models (LLMs) have achieved great success in general domains of natural language processing. In this paper, we bring LLMs to the realm of geoscience with the objective of advancing research and applications in this field. To this end,
Externí odkaz:
http://arxiv.org/abs/2306.05064