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20232026
most citedA Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding

21 citations · 26 across the 6 of their papers we have counts for

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5 papers · 1 filter

cs.CL2025

Can LLMs Refuse Questions They Do Not Know? Measuring Knowledge-Aware Refusal in Factual Tasks

Wenbo Pan, Jie Xu, Qiguang Chen +5

Large Language Models (LLMs) should refuse to answer questions beyond their knowledge. This capability, which we term knowledge-aware refusal, is crucial for factual reliability, w…

cs.CL20251 cited

The Hidden Dimensions of LLM Alignment: A Multi-Dimensional Analysis of Orthogonal Safety Directions

Wenbo Pan, Zhichao Liu, Qiguang Chen +3

Large Language Models' safety-aligned behaviors, such as refusing harmful queries, can be represented by linear directions in activation space. Previous research modeled safety beh…

cs.CL2024

Breaking Language Barriers: Cross-Lingual Continual Pre-Training at Scale

Wenzhen Zheng, Wenbo Pan, Xu Xu +3

In recent years, Large Language Models (LLMs) have made significant strides towards Artificial General Intelligence. However, training these models from scratch requires substantia…

cs.CL20234 cited

End-to-end Task-oriented Dialogue: A Survey of Tasks, Methods, and Future Directions

Libo Qin, Wenbo Pan, Qiguang Chen +5

End-to-end task-oriented dialogue (EToD) can directly generate responses in an end-to-end fashion without modular training, which attracts escalating popularity. The advancement of…

cs.CL202321 cited

A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understanding

Wenbo Pan, Qiguang Chen, Xiao Xu +2

Zero-shot dialogue understanding aims to enable dialogue to track the user's needs without any training data, which has gained increasing attention. In this work, we investigate th…