5 citations · 13 across the 11 of their papers we have counts for
5 papers · 1 filter
TRACE: Discovering Task-Specific Parameter via Adaptation-Aware Probing for Continual Fine-Tuning
Xiaosong Han, Ke Chen, Xindi Dai +7
In real-world deployment, LLMs are often adapted continually across tasks to keep LLMs up-to-date in production, where new fine-tuning should preserve previously learned skills. Ho…
A Simple Graph Contrastive Learning Framework for Short Text Classification
Yonghao Liu, Fausto Giunchiglia, Lan Huang +3
Short text classification has gained significant attention in the information age due to its prevalence and real-world applications. Recent advancements in graph learning combined…
Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning
Yonghao Liu, Mengyu Li, Wei Pang +4
Short text classification, as a research subtopic in natural language processing, is more challenging due to its semantic sparsity and insufficient labeled samples in practical sce…
Resolving Word Vagueness with Scenario-guided Adapter for Natural Language Inference
Yonghao Liu, Mengyu Li, Di Liang +5
Natural Language Inference (NLI) is a crucial task in natural language processing that involves determining the relationship between two sentences, typically referred to as the pre…
Simple-Sampling and Hard-Mixup with Prototypes to Rebalance Contrastive Learning for Text Classification
Mengyu Li, Yonghao Liu, Fausto Giunchiglia +3
Text classification is a crucial and fundamental task in web content mining. Compared with the previous learning paradigm of pre-training and fine-tuning by cross entropy loss, the…