244 citations · 531 across the 46 of their papers we have counts for
18 papers · 1 filter
When Graph Data Meets Multimodal: A New Paradigm for Graph Understanding and Reasoning
Qihang Ai, Jianwu Zhou, Haiyun Jiang +2
Graph data is ubiquitous in the physical world, and it has always been a challenge to efficiently model graph structures using a unified paradigm for the understanding and reasonin…
Hint-enhanced In-Context Learning wakes Large Language Models up for knowledge-intensive tasks
Yifan Wang, Qingyan Guo, Xinzhe Ni +4
In-context learning (ICL) ability has emerged with the increasing scale of large language models (LLMs), enabling them to learn input-label mappings from demonstrations and perform…
Rethinking Word-Level Auto-Completion in Computer-Aided Translation
Xingyu Chen, Lemao Liu, Guoping Huang +4
Word-Level Auto-Completion (WLAC) plays a crucial role in Computer-Assisted Translation. It aims at providing word-level auto-completion suggestions for human translators. While pr…
On Synthetic Data for Back Translation
Jiahao Xu, Yubin Ruan, Wei Bi +4
Back translation (BT) is one of the most significant technologies in NMT research fields. Existing attempts on BT share a common characteristic: they employ either beam search or r…
IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems
Xu Huang, Zhirui Zhang, Ruize Gao +6
We present IMTLab, an open-source end-to-end interactive machine translation (IMT) system platform that enables researchers to quickly build IMT systems with state-of-the-art model…
Repetition In Repetition Out: Towards Understanding Neural Text Degeneration from the Data Perspective
Huayang Li, Tian Lan, Zihao Fu +5
There are a number of diverging hypotheses about the neural text degeneration problem, i.e., generating repetitive and dull loops, which makes this problem both interesting and con…
TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild
Huayang Li, Siheng Li, Deng Cai +5
Large language models with instruction-following abilities have revolutionized the field of artificial intelligence. These models show exceptional generalizability to tackle variou…
Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
Yue Zhang, Yafu Li, Leyang Cui +13
While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit h…
Rethinking Translation Memory Augmented Neural Machine Translation
Hongkun Hao, Guoping Huang, Lemao Liu +3
This paper rethinks translation memory augmented neural machine translation (TM-augmented NMT) from two perspectives, i.e., a probabilistic view of retrieval and the variance-bias…
Sen2Pro: A Probabilistic Perspective to Sentence Embedding from Pre-trained Language Model
Lingfeng Shen, Haiyun Jiang, Lemao Liu +1
Sentence embedding is one of the most fundamental tasks in Natural Language Processing and plays an important role in various tasks. The recent breakthrough in sentence embedding i…
E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition
Zhen Zhang, Mengting Hu, Shiwan Zhao +6
Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER syste…
Frequency-aware Dimension Selection for Static Word Embedding by Mixed Product Distance
Lingfeng Shen, Haiyun Jiang, Lemao Liu +1
Static word embedding is still useful, particularly for context-unavailable tasks, because in the case of no context available, pre-trained language models often perform worse than…
A Simple and Plug-and-play Method for Unsupervised Sentence Representation Enhancement
Lingfeng Shen, Haiyun Jiang, Lemao Liu +1
Generating proper embedding of sentences through an unsupervised way is beneficial to semantic matching and retrieval problems in real-world scenarios. This paper presents Represen…
SimCSE++: Improving Contrastive Learning for Sentence Embeddings from Two Perspectives
Jiahao Xu, Wei Shao, Lihui Chen +1
This paper improves contrastive learning for sentence embeddings from two perspectives: handling dropout noise and addressing feature corruption. Specifically, for the first perspe…
Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection Layer
Ruize Gao, Zhirui Zhang, Yichao Du +2
Nearest Neighbor Machine Translation (NN-MT) has achieved great success in domain adaptation tasks by integrating pre-trained Neural Machine Translation (NMT) models with domain…
Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory
Xin Cheng, Di Luo, Xiuying Chen +3
With direct access to human-written reference as memory, retrieval-augmented generation has achieved much progress in a wide range of text generation tasks. Since better memory wou…
Fairness-guided Few-shot Prompting for Large Language Models
Huan Ma, Changqing Zhang, Yatao Bian +7
Large language models have demonstrated surprising ability to perform in-context learning, i.e., these models can be directly applied to solve numerous downstream tasks by conditio…
Federated Nearest Neighbor Machine Translation
Yichao Du, Zhirui Zhang, Bingzhe Wu +3
To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention. Training neural machine translation (NMT) models with traditional F…