Zobrazeno 1 - 10
of 208
pro vyhledávání: '"Jin, Hongxia"'
Autor:
Lin, Chi-Heng, Gao, Shangqian, Smith, James Seale, Patel, Abhishek, Tuli, Shikhar, Shen, Yilin, Jin, Hongxia, Hsu, Yen-Chang
Large Language Models (LLMs) have reshaped the landscape of artificial intelligence by demonstrating exceptional performance across various tasks. However, substantial computational requirements make their deployment challenging on devices with limit
Externí odkaz:
http://arxiv.org/abs/2408.09632
Question Answer Generation (QAG) is an effective data augmentation technique to improve the accuracy of question answering systems, especially in low-resource domains. While recent pretrained and large language model-based QAG methods have made subst
Externí odkaz:
http://arxiv.org/abs/2406.17990
Traditional language models operate autoregressively, i.e., they predict one token at a time. Rapid explosion in model sizes has resulted in high inference times. In this work, we propose DynaMo, a suite of multi-token prediction language models that
Externí odkaz:
http://arxiv.org/abs/2405.00888
Publikováno v:
Proceedings of 24th INTERSPEECH Conference (INTERSPEECH 2023), Dublin, Ireland
State-of-the-art spoken language understanding (SLU) models have shown tremendous success in benchmark SLU datasets, yet they still fail in many practical scenario due to the lack of model compositionality when trained on limited training data. In th
Externí odkaz:
http://arxiv.org/abs/2312.15815
Open World Compositional Zero-Shot Learning (OW-CZSL) is known to be an extremely challenging task, which aims to recognize unseen compositions formed from seen attributes and objects without any prior assumption of the output space. In order to achi
Externí odkaz:
http://arxiv.org/abs/2312.02191
Vision Transformers (ViTs) have emerged as powerful backbones in computer vision, outperforming many traditional CNNs. However, their computational overhead, largely attributed to the self-attention mechanism, makes deployment on resource-constrained
Externí odkaz:
http://arxiv.org/abs/2312.01026
Recent work has demonstrated a remarkable ability to customize text-to-image diffusion models to multiple, fine-grained concepts in a sequential (i.e., continual) manner while only providing a few example images for each concept. This setting is know
Externí odkaz:
http://arxiv.org/abs/2311.18763
Autor:
Srinivasa, Rakshith Sharma, Cho, Jaejin, Yang, Chouchang, Saidutta, Yashas Malur, Lee, Ching-Hua, Shen, Yilin, Jin, Hongxia
This paper considers contrastive training for cross-modal 0-shot transfer wherein a pre-trained model in one modality is used for representation learning in another domain using pairwise data. The learnt models in the latter domain can then be used f
Externí odkaz:
http://arxiv.org/abs/2309.14580
Joint intent detection and slot filling, which is also termed as joint NLU (Natural Language Understanding) is invaluable for smart voice assistants. Recent advancements in this area have been heavily focusing on improving accuracy using various tech
Externí odkaz:
http://arxiv.org/abs/2309.14485
Autor:
Yan, Jun, Yadav, Vikas, Li, Shiyang, Chen, Lichang, Tang, Zheng, Wang, Hai, Srinivasan, Vijay, Ren, Xiang, Jin, Hongxia
Instruction-tuned Large Language Models (LLMs) have become a ubiquitous platform for open-ended applications due to their ability to modulate responses based on human instructions. The widespread use of LLMs holds significant potential for shaping pu
Externí odkaz:
http://arxiv.org/abs/2307.16888