24 citations · 127 across the 25 of their papers we have counts for
10 papers · 1 filter
An Analysis and Mitigation of the Reversal Curse
Ang Lv, Kaiyi Zhang, Shufang Xie +4
Recent research observed a noteworthy phenomenon in large language models (LLMs), referred to as the ``reversal curse.'' The reversal curse is that when dealing with two entities,…
MolXPT: Wrapping Molecules with Text for Generative Pre-training
Zequn Liu, Wei Zhang, Yingce Xia +5
Generative pre-trained Transformer (GPT) has demonstrates its great success in natural language processing and related techniques have been adapted into molecular modeling. Conside…
What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization
Griffin Adams, Bichlien H Nguyen, Jake Smith +7
Summarization models often generate text that is poorly calibrated to quality metrics because they are trained to maximize the likelihood of a single reference (MLE). To address th…
ResiDual: Transformer with Dual Residual Connections
Shufang Xie, Huishuai Zhang, Junliang Guo +6
Transformer networks have become the preferred architecture for many tasks due to their state-of-the-art performance. However, the optimal way to implement residual connections in…
Building Multilingual Machine Translation Systems That Serve Arbitrary X-Y Translations
Akiko Eriguchi, Shufang Xie, Tao Qin +1
Multilingual Neural Machine Translation (MNMT) enables one system to translate sentences from multiple source languages to multiple target languages, greatly reducing deployment co…
Discovering Drug-Target Interaction Knowledge from Biomedical Literature
Yutai Hou, Yingce Xia, Lijun Wu +6
The Interaction between Drugs and Targets (DTI) in human body plays a crucial role in biomedical science and applications. As millions of papers come out every year in the biomedic…
UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost
Zhen Wu, Lijun Wu, Qi Meng +5
Transformer architecture achieves great success in abundant natural language processing tasks. The over-parameterization of the Transformer model has motivated plenty of works to a…
IOT: Instance-wise Layer Reordering for Transformer Structures
Jinhua Zhu, Lijun Wu, Yingce Xia +5
With sequentially stacked self-attention, (optional) encoder-decoder attention, and feed-forward layers, Transformer achieves big success in natural language processing (NLP), and…
Multi-branch Attentive Transformer
Yang Fan, Shufang Xie, Yingce Xia +4
While the multi-branch architecture is one of the key ingredients to the success of computer vision tasks, it has not been well investigated in natural language processing, especia…
Temporally Correlated Task Scheduling for Sequence Learning
Xueqing Wu, Lewen Wang, Yingce Xia +5
Sequence learning has attracted much research attention from the machine learning community in recent years. In many applications, a sequence learning task is usually associated wi…