Lefei Zhang

Multi-class active learning: A hybrid informative and representative criterion inspired approach

2017

Labeling each instance in a large-scale data set is extremely labor- and time-consuming. One way to alleviate this problem is active learning, which aims to discover the most valuable instances for labeling to construct a …

Multi-Task Learning With Multi-Query Transformer for Dense Prediction

2023 · IEEE Transactions on Circuits and Systems for Video Technology

Previous multi-task dense prediction studies developed complex pipelines such as multi-modal distillations in multiple stages or searching for task relational contexts for each task. The core insight beyond these methods is to maximize the mutual …

Imitate Before Detect: Aligning Machine Stylistic Preference for Machine-Revised Text Detection

2024 · arXiv (Cornell University)

Large Language Models (LLMs) have revolutionized text generation, making detecting machine-generated text increasingly challenging. Although past methods have achieved good performance on detecting pure machine-generated text, those detectors have poor performance on distinguishing machine-revised text …

Segment First or Comprehend First? Explore the Limit of Unsupervised Word Segmentation with Large Language Models

2025 · arXiv (Cornell University)

Word segmentation stands as a cornerstone of Natural Language Processing (NLP). Based on the concept of "comprehend first, segment later", we propose a new framework to explore the limit of unsupervised word segmentation with Large …

Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging

2025 · IEEE Transactions on Pattern Analysis and Machine Intelligence

Multi-task learning (MTL) leverages a shared model to accomplish multiple tasks and facilitate knowledge transfer. Recent research on task arithmetic-based MTL demonstrates that merging the parameters of independently fine-tuned models can effectively achieve MTL. However, …