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pro vyhledávání: '"Yang, Jiuding"'
Large Language Models (LLMs) require precise alignment with complex instructions to optimize their performance in real-world applications. As the demand for refined instruction tuning data increases, traditional methods that evolve simple seed instru
Externí odkaz:
http://arxiv.org/abs/2410.02795
The effective alignment of Large Language Models (LLMs) with precise instructions is essential for their application in diverse real-world scenarios. Current methods focus on enhancing the diversity and complexity of training and evaluation samples,
Externí odkaz:
http://arxiv.org/abs/2406.11301
Ensuring factual consistency between the summary and the original document is paramount in summarization tasks. Consequently, considerable effort has been dedicated to detecting inconsistencies. With the advent of Large Language Models (LLMs), recent
Externí odkaz:
http://arxiv.org/abs/2403.07557
The fine-tuning of Large Language Models (LLMs) specialized in code generation has seen notable advancements through the use of open-domain coding queries. Despite the successes, existing methodologies like Evol-Instruct encounter performance limitat
Externí odkaz:
http://arxiv.org/abs/2312.15692
Current recommender systems employ large-sized embedding tables with uniform dimensions for all features, leading to overfitting, high computational cost, and suboptimal generalizing performance. Many techniques aim to solve this issue by feature sel
Externí odkaz:
http://arxiv.org/abs/2309.07967
With the widespread popularity of user-generated short videos, it becomes increasingly challenging for content creators to promote their content to potential viewers. Automatically generating appealing titles and covers for short videos can help grab
Externí odkaz:
http://arxiv.org/abs/2304.12561
Nested Named Entity Recognition (NNER) has been a long-term challenge to researchers as an important sub-area of Named Entity Recognition. NNER is where one entity may be part of a longer entity, and this may happen on multiple levels, as the term ne
Externí odkaz:
http://arxiv.org/abs/2211.10854
Autor:
Yang, Jiuding, Guo, Weidong, Liu, Bang, Yu, Yakun, Wang, Chaoyue, Luo, Jinwen, Kong, Linglong, Niu, Di, Wen, Zhen
Although conceptualization has been widely studied in semantics and knowledge representation, it is still challenging to find the most accurate concept phrases to characterize the main idea of a text snippet on the fast-growing social media. This is
Externí odkaz:
http://arxiv.org/abs/2110.06892
Autor:
Yang, Jiuding, Guo, Weidong, Liu, Bang, Yu, Yakun, Wang, Chaoyue, Luo, Jinwen, Kong, Linglong, Niu, Di, Wen, Zhen
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Although conceptualization has been widely studied in semantics and knowledge representation, it is still challenging to find the most accurate concept phrases to characterize the main idea of a text snippet on the fast-growing social media. This is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d13c2e2693b90c8ae105d8ea16b3858
https://doi.org/10.1145/3534678.3539077
https://doi.org/10.1145/3534678.3539077
