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pro vyhledávání: '"Liu,Qi"'
Large Language Models (LLMs) have shown remarkable capabilities in various natural language processing tasks. However, LLMs may rely on dataset biases as shortcuts for prediction, which can significantly impair their robustness and generalization cap
Externí odkaz:
http://arxiv.org/abs/2410.13343
Autor:
Jiang, Botian, Li, Lei, Li, Xiaonan, Li, Zhaowei, Feng, Xiachong, Kong, Lingpeng, Liu, Qi, Qiu, Xipeng
The rapid advancement of Multimodal Large Language Models (MLLMs) has been accompanied by the development of various benchmarks to evaluate their capabilities. However, the true nature of these evaluations and the extent to which they assess multimod
Externí odkaz:
http://arxiv.org/abs/2410.12329
Molecular docking, a technique for predicting ligand binding poses, is crucial in structure-based drug design for understanding protein-ligand interactions. Recent advancements in docking methods, particularly those leveraging geometric deep learning
Externí odkaz:
http://arxiv.org/abs/2410.11224
Autor:
Liu, Qi, Ma, Wanjing
In this paper, we identify and analyze a recurring training loss pattern, which we term the \textit{Epochal Sawtooth Effect (ESE)}, commonly observed during training with adaptive gradient-based optimizers, particularly Adam optimizer. This pattern i
Externí odkaz:
http://arxiv.org/abs/2410.10056
Autor:
Li, Lei, Xie, Zhihui, Li, Mukai, Chen, Shunian, Wang, Peiyi, Chen, Liang, Yang, Yazheng, Wang, Benyou, Kong, Lingpeng, Liu, Qi
As large vision-language models (LVLMs) evolve rapidly, the demand for high-quality and diverse data to align these models becomes increasingly crucial. However, the creation of such data with human supervision proves costly and time-intensive. In th
Externí odkaz:
http://arxiv.org/abs/2410.09421
No-reference bitstream-layer point cloud quality assessment (PCQA) can be deployed without full decoding at any network node to achieve real-time quality monitoring. In this work, we focus on the PCQA problem dedicated to Octree-RAHT encoding mode. F
Externí odkaz:
http://arxiv.org/abs/2410.06729
No-reference bitstream-layer point cloud quality assessment (PCQA) can be deployed without full decoding at any network node to achieve real-time quality monitoring. In this work, we develop the first PCQA model dedicated to Trisoup-Lifting encoded 3
Externí odkaz:
http://arxiv.org/abs/2410.06689
Learning recommender systems with multi-class optimization objective is a prevalent setting in recommendation. However, as observed user feedback often accounts for a tiny fraction of the entire item pool, the standard Softmax loss tends to ignore th
Externí odkaz:
http://arxiv.org/abs/2410.06536
Entity Linking (EL) is the process of associating ambiguous textual mentions to specific entities in a knowledge base. Traditional EL methods heavily rely on large datasets to enhance their performance, a dependency that becomes problematic in the co
Externí odkaz:
http://arxiv.org/abs/2410.07549
Large language models have been successfully applied to programming assistance tasks, such as code completion, code insertion, and instructional code editing. However, these applications remain insufficiently automated and struggle to effectively int
Externí odkaz:
http://arxiv.org/abs/2410.07002