9 citations · 19 across the 4 of their papers we have counts for
4 papers
Enough is as good as a feast: A Comprehensive Analysis of How Reinforcement Learning Mitigates Task Conflicts in LLMs
Zixuan Ren, Jinliang Lu, Junhong Wu +5
Model merging plays a crucial role in consolidating multiple specialized models into a single, unified model, especially in the era of large language models (LLMs). Recent research…
Learning In-context Learning for Named Entity Recognition
Jiawei Chen, Yaojie Lu, Hongyu Lin +7
Named entity recognition in real-world applications suffers from the diversity of entity types, the emergence of new entity types, and the lack of high-quality annotations. To addr…
Universal Information Extraction as Unified Semantic Matching
Jie Lou, Yaojie Lu, Dai Dai +5
The challenge of information extraction (IE) lies in the diversity of label schemas and the heterogeneity of structures. Traditional methods require task-specific model design and…
Unified Structure Generation for Universal Information Extraction
Yaojie Lu, Qing Liu, Dai Dai +5
Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas. In this paper, we propose a unified text-to-structure generation fra…