Zobrazeno 1 - 10
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pro vyhledávání: '"Gu, Jia-Chen"'
Retrieval-augmented language models (RALMs) have shown strong performance and wide applicability in knowledge-intensive tasks. However, there are significant trustworthiness concerns as RALMs are prone to generating unfaithful outputs, including base
Externí odkaz:
http://arxiv.org/abs/2406.13692
Model editing is an emerging field that focuses on updating the knowledge embedded within large language models (LLMs) without extensive retraining. However, current model editing methods significantly compromise the general abilities of LLMs as the
Externí odkaz:
http://arxiv.org/abs/2405.16821
Autor:
Zhu, Yun, Gu, Jia-Chen, Sikora, Caitlin, Ko, Ho, Liu, Yinxiao, Lin, Chu-Cheng, Shu, Lei, Luo, Liangchen, Meng, Lei, Liu, Bang, Chen, Jindong
Large language models (LLMs) augmented with retrieval exhibit robust performance and extensive versatility by incorporating external contexts. However, the input length grows linearly in the number of retrieved documents, causing a dramatic increase
Externí odkaz:
http://arxiv.org/abs/2405.16178
Audio-text retrieval (ATR), which retrieves a relevant caption given an audio clip (A2T) and vice versa (T2A), has recently attracted much research attention. Existing methods typically aggregate information from each modality into a single vector fo
Externí odkaz:
http://arxiv.org/abs/2403.10146
Despite their exceptional capabilities, large language models (LLMs) are prone to generating unintended text due to false or outdated knowledge. Given the resource-intensive nature of retraining LLMs, there has been a notable increase in the developm
Externí odkaz:
http://arxiv.org/abs/2401.17623
Large language models (LLMs) inevitably exhibit hallucinations since the accuracy of generated texts cannot be secured solely by the parametric knowledge they encapsulate. Although retrieval-augmented generation (RAG) is a practicable complement to L
Externí odkaz:
http://arxiv.org/abs/2401.15884
Autor:
Li, Zhen, Xu, Xiaohan, Shen, Tao, Xu, Can, Gu, Jia-Chen, Lai, Yuxuan, Tao, Chongyang, Ma, Shuai
In the rapidly evolving domain of Natural Language Generation (NLG) evaluation, introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance. This paper ai
Externí odkaz:
http://arxiv.org/abs/2401.07103
Autor:
Gu, Jia-Chen, Xu, Hao-Xiang, Ma, Jun-Yu, Lu, Pan, Ling, Zhen-Hua, Chang, Kai-Wei, Peng, Nanyun
Model editing is a technique that edits the large language models (LLMs) with updated knowledge to alleviate hallucinations without resource-intensive retraining. While current model editing methods can effectively modify a model's behavior within a
Externí odkaz:
http://arxiv.org/abs/2401.04700
Autor:
Zhang, Ningyu, Yao, Yunzhi, Tian, Bozhong, Wang, Peng, Deng, Shumin, Wang, Mengru, Xi, Zekun, Mao, Shengyu, Zhang, Jintian, Ni, Yuansheng, Cheng, Siyuan, Xu, Ziwen, Xu, Xin, Gu, Jia-Chen, Jiang, Yong, Xie, Pengjun, Huang, Fei, Liang, Lei, Zhang, Zhiqiang, Zhu, Xiaowei, Zhou, Jun, Chen, Huajun
Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. However, a primary limitation lies in the significant computational demands during training, arising fro
Externí odkaz:
http://arxiv.org/abs/2401.01286
Large Language Models (LLMs) have emerged as influential instruments within the realm of natural language processing; nevertheless, their capacity to handle multi-party conversations (MPCs) -- a scenario marked by the presence of multiple interlocuto
Externí odkaz:
http://arxiv.org/abs/2310.16301