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of 495
pro vyhledávání: '"Zhu Wenhao"'
Reasoning capabilities are crucial for Large Language Models (LLMs), yet a notable gap exists between English and non-English languages. To bridge this disparity, some works fine-tune LLMs to relearn reasoning capabilities in non-English languages, w
Externí odkaz:
http://arxiv.org/abs/2405.17386
Autor:
Zhang, Shimao, Gao, Changjiang, Zhu, Wenhao, Chen, Jiajun, Huang, Xin, Han, Xue, Feng, Junlan, Deng, Chao, Huang, Shujian
Recently, Large Language Models (LLMs) have shown impressive language capabilities. While most of the existing LLMs have very unbalanced performance across different languages, multilingual alignment based on translation parallel data is an effective
Externí odkaz:
http://arxiv.org/abs/2405.13816
Graph Transformers (GTs) have significantly advanced the field of graph representation learning by overcoming the limitations of message-passing graph neural networks (GNNs) and demonstrating promising performance and expressive power. However, the q
Externí odkaz:
http://arxiv.org/abs/2405.03481
We obtain several inequalities on the generalized means of dependent p-values. In particular, the weighted harmonic mean of p-values is strictly sub-uniform under several dependence assumptions of p-values, including independence, weak negative assoc
Externí odkaz:
http://arxiv.org/abs/2405.01368
Bridging the significant gap between large language model's English and non-English performance presents a great challenge. While some previous studies attempt to mitigate this gap with translated training data, the recently proposed question alignme
Externí odkaz:
http://arxiv.org/abs/2405.01345
Autor:
Zeng Bingxin, Zhu Wenhao
Publikováno v:
SHS Web of Conferences, Vol 163, p 03031 (2023)
To cope with global climate change, a growing number of countries have formulated carbon neutrality schedules. In this context, it is increasingly important for governments to design and implement the policy package to achieve this goal. This paper s
Externí odkaz:
https://doaj.org/article/bc96ce2fbfdc4cb4bda000b192c540d5
Missing datasets, in which some objects have missing values in certain dimensions, are prevalent in the Real-world. Existing clustering algorithms for missing datasets first impute the missing values and then perform clustering. However, both the imp
Externí odkaz:
http://arxiv.org/abs/2404.05363
Large language models show compelling performance on reasoning tasks but they tend to perform much worse in languages other than English. This is unsurprising given that their training data largely consists of English text and instructions. A typical
Externí odkaz:
http://arxiv.org/abs/2401.07817
Though reasoning abilities are considered language-agnostic, existing LLMs exhibit inconsistent reasoning abilities across different languages, e.g., reasoning in the dominant language like English is superior to other languages due to the imbalance
Externí odkaz:
http://arxiv.org/abs/2401.06838
Publikováno v:
IEEE Access, Vol 6, Pp 72724-72734 (2018)
Educational data-mining is an evolving discipline that focuses on the improvement of self-learning and adaptive methods. It is used for finding hidden patterns or intrinsic structures of educational data. In the arena of education, the heterogeneous
Externí odkaz:
https://doaj.org/article/c9771556c689459683f47384ed84e472