36 citations · 93 across the 10 of their papers we have counts for
10 papers
Tuna: Instruction Tuning using Feedback from Large Language Models
Haoran Li, Yiran Liu, Xingxing Zhang +2
Instruction tuning of open-source large language models (LLMs) like LLaMA, using direct outputs from more powerful LLMs such as Instruct-GPT and GPT-4, has proven to be a cost-effe…
LongNet: Scaling Transformers to 1,000,000,000 Tokens
Jiayu Ding, Shuming Ma, Li Dong +5
Scaling sequence length has become a critical demand in the era of large language models. However, existing methods struggle with either computational complexity or model expressiv…
Latent Prompt Tuning for Text Summarization
Yubo Zhang, Xingxing Zhang, Xun Wang +2
Prompts with different control signals (e.g., length, keywords, etc.) can be used to control text summarization. When control signals are available, they can control the properties…
Unsupervised Multi-Granularity Summarization
Ming Zhong, Yang Liu, Suyu Ge +7
Text summarization is a user-preference based task, i.e., for one document, users often have different priorities for summary. As a key aspect of customization in summarization, gr…
Sequence Level Contrastive Learning for Text Summarization
Shusheng Xu, Xingxing Zhang, Yi Wu +1
Contrastive learning models have achieved great success in unsupervised visual representation learning, which maximize the similarities between feature representations of different…
Attention Temperature Matters in Abstractive Summarization Distillation
Shengqiang Zhang, Xingxing Zhang, Hangbo Bao +1
Recent progress of abstractive text summarization largely relies on large pre-trained sequence-to-sequence Transformer models, which are computationally expensive. This paper aims…
Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers
Shusheng Xu, Xingxing Zhang, Yi Wu +2
Unsupervised extractive document summarization aims to select important sentences from a document without using labeled summaries during training. Existing methods are mostly graph…
Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction
Mengyun Chen, Tao Ge, Xingxing Zhang +2
We propose a novel language-independent approach to improve the efficiency for Grammatical Error Correction (GEC) by dividing the task into two subtasks: Erroneous Span Detection (…
Pre-training for Abstractive Document Summarization by Reinstating Source Text
Yanyan Zou, Xingxing Zhang, Wei Lu +2
Abstractive document summarization is usually modeled as a sequence-to-sequence (Seq2Seq) learning problem. Unfortunately, training large Seq2Seq based summarization models on limi…
DualVD: An Adaptive Dual Encoding Model for Deep Visual Understanding in Visual Dialogue
Xiaoze Jiang, Jing Yu, Zengchang Qin +4
Different from Visual Question Answering task that requires to answer only one question about an image, Visual Dialogue involves multiple questions which cover a broad range of vis…