Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Wang, William"'
Autor:
Antoniades, Antonis, Wang, Xinyi, Elazar, Yanai, Amayuelas, Alfonso, Albalak, Alon, Zhang, Kexun, Wang, William Yang
Despite the proven utility of large language models (LLMs) in real-world applications, there remains a lack of understanding regarding how they leverage their large-scale pretraining text corpora to achieve such capabilities. In this work, we investi
Externí odkaz:
http://arxiv.org/abs/2407.14985
Autor:
Dickens, Charles, Pryor, Connor, Gao, Changyu, Albalak, Alon, Augustine, Eriq, Wang, William, Wright, Stephen, Getoor, Lise
The field of Neural-Symbolic (NeSy) systems is growing rapidly. Proposed approaches show great promise in achieving symbiotic unions of neural and symbolic methods. However, each NeSy system differs in fundamental ways. There is a pressing need for a
Externí odkaz:
http://arxiv.org/abs/2407.09693
Large language models (LLMs) have shown remarkable performance on code generation tasks. A recent application of LLMs for code generation is iterative code repair, where a model fixes an incorrect program by rationalizing about errors and generating
Externí odkaz:
http://arxiv.org/abs/2406.14867
Autor:
Wang, Danqing, Antoniades, Antonis, Luong, Kha-Dinh, Zhang, Edwin, Kosan, Mert, Li, Jiachen, Singh, Ambuj, Wang, William Yang, Li, Lei
Counterfactual explanations of Graph Neural Networks (GNNs) offer a powerful way to understand data that can naturally be represented by a graph structure. Furthermore, in many domains, it is highly desirable to derive data-driven global explanations
Externí odkaz:
http://arxiv.org/abs/2406.13869
Direct alignment from preferences (DAP) has emerged as a promising paradigm for aligning large language models (LLMs) to human desiderata from pre-collected, offline preference datasets. While recent studies indicate that existing offline DAP methods
Externí odkaz:
http://arxiv.org/abs/2406.12168
Autor:
Albalak, Alon, Elazar, Yanai, Xie, Sang Michael, Longpre, Shayne, Lambert, Nathan, Wang, Xinyi, Muennighoff, Niklas, Hou, Bairu, Pan, Liangming, Jeong, Haewon, Raffel, Colin, Chang, Shiyu, Hashimoto, Tatsunori, Wang, William Yang
A major factor in the recent success of large language models is the use of enormous and ever-growing text datasets for unsupervised pre-training. However, naively training a model on all available data may not be optimal (or feasible), as the qualit
Externí odkaz:
http://arxiv.org/abs/2402.16827
Autor:
Wang, Xinyi, Amayuelas, Alfonso, Zhang, Kexun, Pan, Liangming, Chen, Wenhu, Wang, William Yang
Pre-trained language models (LMs) are able to perform complex reasoning without explicit fine-tuning. To understand how pre-training with a next-token prediction objective contributes to the emergence of such reasoning capability, we propose that we
Externí odkaz:
http://arxiv.org/abs/2402.03268
As the number of accepted papers at AI and ML conferences reaches into the thousands, it has become unclear how researchers access and read research publications. In this paper, we investigate the role of social media influencers in enhancing the vis
Externí odkaz:
http://arxiv.org/abs/2401.13782
The data used to pretrain large language models has a decisive impact on a model's downstream performance, which has led to a large body of work on data selection methods that aim to automatically determine the most suitable data to use for pretraini
Externí odkaz:
http://arxiv.org/abs/2312.02406
State-of-the-art systems neuroscience experiments yield large-scale multimodal data, and these data sets require new tools for analysis. Inspired by the success of large pretrained models in vision and language domains, we reframe the analysis of lar
Externí odkaz:
http://arxiv.org/abs/2311.00136