6 citations · 16 across the 7 of their papers we have counts for
4 papers · 1 filter
Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization
Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen +2
With the recent undeniable advancement in reasoning abilities in large language models (LLMs) like ChatGPT and GPT-4, there is a growing trend for using LLMs on various tasks. One…
A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization
Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen +2
Pre-trained language models (PLMs) have achieved outstanding achievements in abstractive single-document summarization (SDS). However, such benefits may not fully extend to multi-d…
Hierarchical Dialogue Understanding with Special Tokens and Turn-level Attention
Xiao Liu, Jian Zhang, Heng Zhang +2
Compared with standard text, understanding dialogue is more challenging for machines as the dynamic and unexpected semantic changes in each turn. To model such inconsistent semanti…
Inconsistencies in Masked Language Models
Tom Young, Yunan Chen, Yang You
Learning to predict masked tokens in a sequence has been shown to be a helpful pretraining objective for powerful language models such as PaLM2. After training, such masked languag…