22 citations · 71 across the 10 of their papers we have counts for
10 papers
Training-Free Long-Context Scaling of Large Language Models
Chenxin An, Fei Huang, Jun Zhang +4
The ability of Large Language Models (LLMs) to process and generate coherent text is markedly weakened when the number of input tokens exceeds their pretraining length. Given the e…
Scaling Laws of RoPE-based Extrapolation
Xiaoran Liu, Hang Yan, Shuo Zhang +3
The extrapolation capability of Large Language Models (LLMs) based on Rotary Position Embedding is currently a topic of considerable interest. The mainstream approach to addressing…
L-Eval: Instituting Standardized Evaluation for Long Context Language Models
Chenxin An, Shansan Gong, Ming Zhong +5
Recently, there has been growing interest in extending the context length of large language models (LLMs), aiming to effectively process long inputs of one turn or conversations wi…
Optimizing Non-Autoregressive Transformers with Contrastive Learning
Chenxin An, Jiangtao Feng, Fei Huang +2
Non-autoregressive Transformers (NATs) reduce the inference latency of Autoregressive Transformers (ATs) by predicting words all at once rather than in sequential order. They have…
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…
CoNT: Contrastive Neural Text Generation
Chenxin An, Jiangtao Feng, Kai Lv +3
Recently, contrastive learning attracts increasing interests in neural text generation as a new solution to alleviate the exposure bias problem. It introduces a sequence-level trai…
TURNER: The Uncertainty-based Retrieval Framework for Chinese NER
Zhichao Geng, Hang Yan, Zhangyue Yin +2
Chinese NER is a difficult undertaking due to the ambiguity of Chinese characters and the absence of word boundaries. Previous work on Chinese NER focus on lexicon-based methods to…
-Tuning: An Efficient Tuning Paradigm for Large-Scale Pre-Trained Models via Label Representation Learning
Yitao Liu, Chenxin An, Xipeng Qiu
With the success of large-scale pre-trained models (PTMs), how efficiently adapting PTMs to downstream tasks has attracted tremendous attention, especially for PTMs with billions o…
RetrievalSum: A Retrieval Enhanced Framework for Abstractive Summarization
Chenxin An, Ming Zhong, Zhichao Geng +2
Existing summarization systems mostly generate summaries purely relying on the content of the source document. However, even for humans, we usually need some references or exemplar…
Enhancing Scientific Papers Summarization with Citation Graph
Chenxin An, Ming Zhong, Yiran Chen +3
Previous work for text summarization in scientific domain mainly focused on the content of the input document, but seldom considering its citation network. However, scientific pape…