35 citations · 104 across the 27 of their papers we have counts for
10 papers · 1 filter
ZeroKBC: A Comprehensive Benchmark for Zero-Shot Knowledge Base Completion
Pei Chen, Wenlin Yao, Hongming Zhang +4
Knowledge base completion (KBC) aims to predict the missing links in knowledge graphs. Previous KBC tasks and approaches mainly focus on the setting where all test entities and rel…
Join-Chain Network: A Logical Reasoning View of the Multi-head Attention in Transformer
Jianyi Zhang, Yiran Chen, Jianshu Chen
Developing neural architectures that are capable of logical reasoning has become increasingly important for a wide range of applications (e.g., natural language processing). Toward…
Z-LaVI: Zero-Shot Language Solver Fueled by Visual Imagination
Yue Yang, Wenlin Yao, Hongming Zhang +3
Large-scale pretrained language models have made significant advances in solving downstream language understanding tasks. However, they generally suffer from reporting bias, the ph…
Explanations from Large Language Models Make Small Reasoners Better
Shiyang Li, Jianshu Chen, Yelong Shen +9
Integrating free-text explanations to in-context learning of large language models (LLM) is shown to elicit strong reasoning capabilities along with reasonable explanations. In thi…
Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models
Xiaoman Pan, Wenlin Yao, Hongming Zhang +3
Fully-parametric language models generally require a huge number of model parameters to store the necessary knowledge for solving multiple natural language tasks in zero/few-shot s…
Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks
Zhenhailong Wang, Xiaoman Pan, Dian Yu +3
Although large language models have achieved impressive zero-shot ability, the huge model size generally incurs high cost. Recently, semi-parametric language models, which augment…
Blessing of Class Diversity in Pre-training
Yulai Zhao, Jianshu Chen, Simon S. Du
This paper presents a new statistical analysis aiming to explain the recent superior achievements of the pre-training techniques in natural language processing (NLP). We prove that…
C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of References
Xiang Yue, Xiaoman Pan, Wenlin Yao +3
We consider the problem of pretraining a two-stage open-domain question answering (QA) system (retriever + reader) with strong transfer capabilities. The key challenge is how to co…
Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension
Chao Zhao, Wenlin Yao, Dian Yu +3
Comprehending a dialogue requires a model to capture diverse kinds of key information in the utterances, which are either scattered around or implicitly implied in different turns…
PRIMA: Planner-Reasoner Inside a Multi-task Reasoning Agent
Daoming Lyu, Bo Liu, Jianshu Chen
We consider the problem of multi-task reasoning (MTR), where an agent can solve multiple tasks via (first-order) logic reasoning. This capability is essential for human-like intell…