23 citations · 32 across the 3 of their papers we have counts for
3 papers
cs.CL2023★ 23 cited
Language Models Meet World Models: Embodied Experiences Enhance Language Models
Jiannan Xiang, Tianhua Tao, Yi Gu +4
While large language models (LMs) have shown remarkable capabilities across numerous tasks, they often struggle with simple reasoning and planning in physical environments, such as…
cs.CL2022★ 5 cited
On the Learning of Non-Autoregressive Transformers
Fei Huang, Tianhua Tao, Hao Zhou +2
Non-autoregressive Transformer (NAT) is a family of text generation models, which aims to reduce the decoding latency by predicting the whole sentences in parallel. However, such l…
cs.CL2021★ 4 cited
Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
Guangyi Liu, Zichao Yang, Tianhua Tao +6
Neural text generation models are typically trained by maximizing log-likelihood with the sequence cross entropy (CE) loss, which encourages an exact token-by-token match between a…