257 citations · 519 across the 28 of their papers we have counts for
9 papers · 1 filter
Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering
Zhiyong Wu, Yaoxiang Wang, Jiacheng Ye +1
Despite the surprising few-shot performance of in-context learning (ICL), it is still a common practice to randomly sample examples to serve as context. This paper advocates a new…
Explanation Regeneration via Information Bottleneck
Qintong Li, Zhiyong Wu, Lingpeng Kong +1
Explaining the black-box predictions of NLP models naturally and accurately is an important open problem in natural language generation. These free-text explanations are expected t…
Unsupervised Explanation Generation via Correct Instantiations
Sijie Cheng, Zhiyong Wu, Jiangjie Chen +3
While large pre-trained language models (PLM) have shown their great skills at solving discriminative tasks, a significant gap remains when compared with humans for explanation-rel…
ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback
Jiacheng Ye, Jiahui Gao, Jiangtao Feng +3
Recently, dataset-generation-based zero-shot learning has shown promising results by training a task-specific model with a dataset synthesized from large pre-trained language model…
DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models
Shansan Gong, Mukai Li, Jiangtao Feng +2
Recently, diffusion models have emerged as a new paradigm for generative models. Despite the success in domains using continuous signals such as vision and audio, adapting diffusio…
COLO: A Contrastive Learning based Re-ranking Framework for One-Stage Summarization
Chenxin An, Ming Zhong, Zhiyong Wu +3
Traditional training paradigms for extractive and abstractive summarization systems always only use token-level or sentence-level training objectives. However, the output summary i…
Lexical Knowledge Internalization for Neural Dialog Generation
Zhiyong Wu, Wei Bi, Xiang Li +2
We propose knowledge internalization (KI), which aims to complement the lexical knowledge into neural dialog models. Instead of further conditioning the knowledge-grounded dialog (…
Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning
Jiahui Gao, Renjie Pi, Yong Lin +7
There is a rising interest in further exploring the zero-shot learning potential of large pre-trained language models (PLMs). A new paradigm called data-generation-based zero-shot…
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
Jiacheng Ye, Jiahui Gao, Qintong Li +5
There is a growing interest in dataset generation recently due to the superior generative capacity of large pre-trained language models (PLMs). In this paper, we study a flexible a…