activity
20202025
most citedAutoHint: Automatic Prompt Optimization with Hint Generation

4 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2025

Preference is More Than Comparisons: Rethinking Dueling Bandits with Augmented Human Feedback

Shengbo Wang, Hong Sun, Ke Li

Interactive preference elicitation (IPE) aims to substantially reduce human effort while acquiring human preferences in wide personalization systems. Dueling bandit (DB) algorithms…

cs.CV2025

Beyond Label Semantics: Language-Guided Action Anatomy for Few-shot Action Recognition

Zefeng Qian, Xincheng Yao, Yifei Huang +3

Few-shot action recognition (FSAR) aims to classify human actions in videos with only a small number of labeled samples per category. The scarcity of training data has driven recen…

cs.CL2024

WPN: An Unlearning Method Based on N-pair Contrastive Learning in Language Models

Guitao Chen, Yunshen Wang, Hongye Sun +1

Generative language models (LMs) offer numerous advantages but may produce inappropriate or harmful outputs due to the harmful knowledge acquired during pre-training. This knowledg…

cs.CL2023★ 4 cited

AutoHint: Automatic Prompt Optimization with Hint Generation

Hong Sun, Xue Li, Yinchuan Xu +5

This paper presents AutoHint, a novel framework for automatic prompt engineering and optimization for Large Language Models (LLM). While LLMs have demonstrated remarkable ability i…

cs.CL2020★ 1 cited

AutoADR: Automatic Model Design for Ad Relevance

Yiren Chen, Yaming Yang, Hong Sun +7

Large-scale pre-trained models have attracted extensive attention in the research community and shown promising results on various tasks of natural language processing. However, th…