Jeff Z. Pan

Knowledge-based Transfer Learning Explanation

2018 · arXiv (Cornell University)

Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation, especially for transfer learning, an important machine learning branch that aims at …

Ontology-guided Semantic Composition for Zero-Shot Learning

2020 · arXiv (Cornell University)

Zero-shot learning (ZSL) is a popular research problem that aims at predicting for those classes that have never appeared in the training stage by utilizing the inter-class relationship with some side information. In this study, …

Code-Switching with Word Senses for Pretraining in Neural Machine Translation

2023 · arXiv (Cornell University)

Lexical ambiguity is a significant and pervasive challenge in Neural Machine Translation (NMT), with many state-of-the-art (SOTA) NMT systems struggling to handle polysemous words (Campolungo et al., 2022). The same holds for the NMT pretraining …

UniMS-RAG: A Unified Multi-source Retrieval-Augmented Generation for Personalized Dialogue Systems

2024 · arXiv (Cornell University)

Large Language Models (LLMs) has shown exceptional capabilities in many natual language understanding and generation tasks. However, the personalization issue still remains a much-coveted property, especially when it comes to the multiple sources involved in …

AQE-RF: An Adaptive Quantifier Extension and Rule-Filtering Graph Network for Logical Reasoning of Text

2025 · IEEE Transactions on Neural Networks and Learning Systems

Logical reasoning of text requires neural models to possess strong contextual comprehension and logical reasoning ability to draw conclusions from limited information. To improve the logical reasoning capabilities of pretrained language models (PLMs), existing approaches …

Self-Reasoning Language Models: Unfold Hidden Reasoning Chains with Few Reasoning Catalyst

2025

Inference-time scaling has attracted much attention which significantly enhance the performance of Large Language Models (LLMs) in complex reasoning tasks by increasing the length of Chain-of-Thought.These longer intermediate reasoning rationales embody various meta-reasoning skills in …