Zobrazeno 1 - 10
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pro vyhledávání: '"XIE, Tong"'
Autor:
Xie, Tong, Wan, Yuwei, Liu, Yixuan, Zeng, Yuchen, Zhang, Wenjie, Kit, Chunyu, Zhou, Dongzhan, Hoex, Bram
Materials discovery and design aim to find components and structures with desirable properties over highly complex and diverse search spaces. Traditional solutions, such as high-throughput simulations and machine learning (ML), often rely on complex
Externí odkaz:
http://arxiv.org/abs/2412.11970
Large language models (LLMs) have made significant strides at code generation through improved model design, training, and chain-of-thought. However, prompt-level optimizations remain an important yet under-explored aspect of LLMs for coding. This wo
Externí odkaz:
http://arxiv.org/abs/2412.02906
Autor:
Xie, Tong, Zhang, Hanzhi, Wang, Shaozhou, Wan, Yuwei, Razzak, Imran, Kit, Chunyu, Zhang, Wenjie, Hoex, Bram
Natural Language Processing (NLP) is widely used to supply summarization ability from long context to structured information. However, extracting structured knowledge from scientific text by NLP models remains a challenge because of its domain-specif
Externí odkaz:
http://arxiv.org/abs/2411.12000
Exploring the predictive capabilities of language models in material science is an ongoing interest. This study investigates the application of language model embeddings to enhance material property prediction in materials science. By evaluating vari
Externí odkaz:
http://arxiv.org/abs/2410.16165
Autor:
Yang, Zonglin, Liu, Wanhao, Gao, Ben, Xie, Tong, Li, Yuqiang, Ouyang, Wanli, Poria, Soujanya, Cambria, Erik, Zhou, Dongzhan
Scientific discovery contributes largely to human society's prosperity, and recent progress shows that LLMs could potentially catalyze this process. However, it is still unclear whether LLMs can discover novel and valid hypotheses in chemistry. In th
Externí odkaz:
http://arxiv.org/abs/2410.07076
Autor:
Zhang, Di, Wu, Jianbo, Lei, Jingdi, Che, Tong, Li, Jiatong, Xie, Tong, Huang, Xiaoshui, Zhang, Shufei, Pavone, Marco, Li, Yuqiang, Ouyang, Wanli, Zhou, Dongzhan
This paper presents an advanced mathematical problem-solving framework, LLaMA-Berry, for enhancing the mathematical reasoning ability of Large Language Models (LLMs). The framework combines Monte Carlo Tree Search (MCTS) with iterative Self-Refine to
Externí odkaz:
http://arxiv.org/abs/2410.02884
Autor:
Wan, Yuwei, Liu, Yixuan, Ajith, Aswathy, Grazian, Clara, Hoex, Bram, Zhang, Wenjie, Kit, Chunyu, Xie, Tong, Foster, Ian
We introduce SciQAG, a novel framework for automatically generating high-quality science question-answer pairs from a large corpus of scientific literature based on large language models (LLMs). SciQAG consists of a QA generator and a QA evaluator, w
Externí odkaz:
http://arxiv.org/abs/2405.09939
Autor:
Ye, Yanpeng, Ren, Jie, Wang, Shaozhou, Wan, Yuwei, Wang, Haofen, Razzak, Imran, Hoex, Bram, Xie, Tong, Zhang, Wenjie
Knowledge in materials science is widely dispersed across extensive scientific literature, posing significant challenges for efficient discovery and integration of new materials. Traditional methods, often reliant on costly and time-consuming experim
Externí odkaz:
http://arxiv.org/abs/2404.03080
Stochastic computing (SC) has emerged as a promising computing paradigm for neural acceleration. However, how to accelerate the state-of-the-art Vision Transformer (ViT) with SC remains unclear. Unlike convolutional neural networks, ViTs introduce no
Externí odkaz:
http://arxiv.org/abs/2402.12820
Data attribution methods trace model behavior back to its training dataset, offering an effective approach to better understand ''black-box'' neural networks. While prior research has established quantifiable links between model output and training d
Externí odkaz:
http://arxiv.org/abs/2401.09031