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pro vyhledávání: '"liu, Kang"'
Large language models encapsulate knowledge and have demonstrated superior performance on various natural language processing tasks. Recent studies have localized this knowledge to specific model parameters, such as the MLP weights in intermediate la
Externí odkaz:
http://arxiv.org/abs/2409.00617
Pretrained language models like BERT and T5 serve as crucial backbone encoders for dense retrieval. However, these models often exhibit limited generalization capabilities and face challenges in improving in domain accuracy. Recent research has explo
Externí odkaz:
http://arxiv.org/abs/2408.12194
LLM have achieved success in many fields but still troubled by problematic content in the training corpora. LLM unlearning aims at reducing their influence and avoid undesirable behaviours. However, existing unlearning methods remain vulnerable to ad
Externí odkaz:
http://arxiv.org/abs/2408.10682
In the realm of event prediction, temporal knowledge graph forecasting (TKGF) stands as a pivotal technique. Previous approaches face the challenges of not utilizing experience during testing and relying on a single short-term history, which limits a
Externí odkaz:
http://arxiv.org/abs/2408.07840
Knowledge editing aims to update outdated or incorrect knowledge in large language models (LLMs). However, current knowledge editing methods have limited scalability for lifelong editing. This study explores the fundamental reason why knowledge editi
Externí odkaz:
http://arxiv.org/abs/2408.07413
Graph Convolutional Neural Network (GCN), a widely adopted method for analyzing relational data, enhances node discriminability through the aggregation of neighboring information. Usually, stacking multiple layers can improve the performance of GCN b
Externí odkaz:
http://arxiv.org/abs/2408.03152
Enabling Large Language Models (LLMs) to generate citations in Question-Answering (QA) tasks is an emerging paradigm aimed at enhancing the verifiability of their responses when LLMs are utilizing external references to generate an answer. However, t
Externí odkaz:
http://arxiv.org/abs/2408.04662
In this paper, we introduce semi-autonomous neural ordinary differential equations (SA-NODEs), a variation of the vanilla NODEs, employing fewer parameters. We investigate the universal approximation properties of SA-NODEs for dynamical systems from
Externí odkaz:
http://arxiv.org/abs/2407.17092
Autor:
Sun, Wangtao, Zhang, Chenxiang, Zhang, Xueyou, Huang, Ziyang, Xu, Haotian, Chen, Pei, He, Shizhu, Zhao, Jun, Liu, Kang
Although Large Language Models (LLMs) have demonstrated strong instruction-following ability, they are further supposed to be controlled and guided by rules in real-world scenarios to be safe, accurate, and intelligent. This demands the possession of
Externí odkaz:
http://arxiv.org/abs/2407.08440
Although Large Language Models (LLMs) excel in NLP tasks, they still need external tools to extend their ability. Current research on tool learning with LLMs often assumes mandatory tool use, which does not always align with real-world situations, wh
Externí odkaz:
http://arxiv.org/abs/2407.12823