110 citations · 217 across the 19 of their papers we have counts for
19 papers
Probing Representations for Document-level Event Extraction
Barry Wang, Xinya Du, Claire Cardie
The probing classifiers framework has been employed for interpreting deep neural network models for a variety of natural language processing (NLP) applications. Studies, however, h…
Large Language Models for Automated Open-domain Scientific Hypotheses Discovery
Zonglin Yang, Xinya Du, Junxian Li +3
Hypothetical induction is recognized as the main reasoning type when scientists make observations about the world and try to propose hypotheses to explain those observations. Past…
Proto-CLIP: Vision-Language Prototypical Network for Few-Shot Learning
Jishnu Jaykumar P, Kamalesh Palanisamy, Yu-Wei Chao +2
We propose a novel framework for few-shot learning by leveraging large-scale vision-language models such as CLIP. Motivated by unimodal prototypical networks for few-shot learning,…
PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations
Ruosen Li, Teerth Patel, Xinya Du
Nowadays, the quality of responses generated by different modern large language models (LLMs) is hard to evaluate and compare automatically. Recent studies suggest and predominantl…
Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning
Chi Han, Qizheng He, Charles Yu +3
Probabilistic logical rule learning has shown great strength in logical rule mining and knowledge graph completion. It learns logical rules to predict missing edges by reasoning on…
Logical Reasoning over Natural Language as Knowledge Representation: A Survey
Zonglin Yang, Xinya Du, Rui Mao +2
Logical reasoning is central to human cognition and intelligence. It includes deductive, inductive, and abductive reasoning. Past research of logical reasoning within AI uses forma…