Wanjun Zhong

Improving Question Answering by Commonsense-Based Pre-Training

2018 · arXiv (Cornell University)

Although neural network approaches achieve remarkable success on a variety of NLP tasks, many of them struggle to answer questions that require commonsense knowledge. We believe the main reason is the lack of commonsense \mbox{connections} …

Learning to Edit: Aligning LLMs with Knowledge Editing

2024 · arXiv (Cornell University)

Knowledge editing techniques, aiming to efficiently modify a minor proportion of knowledge in large language models (LLMs) without negatively impacting performance across other inputs, have garnered widespread attention. However, existing methods predominantly rely on memorizing …

Concise and Precise Context Compression for Tool-Using Language Models

2024

Yang Xu, Yunlong Feng, Honglin Mu, Yutai Hou, Yitong Li, Xinghao Wang, Wanjun Zhong, Zhongyang Li, Dandan Tu, Qingfu Zhu, Min Zhang, Wanxiang Che. Findings of the Association for Computational Linguistics: ACL 2024. 2024.

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 …