23 citations · 35 across the 19 of their papers we have counts for
9 papers · 1 filter
Cross-domain Named Entity Recognition via Graph Matching
Junhao Zheng, Haibin Chen, Qianli Ma
Cross-domain NER is a practical yet challenging problem since the data scarcity in the real-world scenario. A common practice is first to learn a NER model in a rich-resource gener…
Pathformer: Recursive Path Query Encoding for Complex Logical Query Answering
Chongzhi Zhang, Zhiping Peng, Junhao Zheng +3
Complex Logical Query Answering (CLQA) over incomplete knowledge graphs is a challenging task. Recently, Query Embedding (QE) methods are proposed to solve CLQA by performing multi…
Towards Lifelong Learning of Large Language Models: A Survey
Junhao Zheng, Shengjie Qiu, Chengming Shi +1
As the applications of large language models (LLMs) expand across diverse fields, the ability of these models to adapt to ongoing changes in data, tasks, and user preferences becom…
Self-Adaptive Reconstruction with Contrastive Learning for Unsupervised Sentence Embeddings
Junlong Liu, Xichen Shang, Huawen Feng +2
Unsupervised sentence embeddings task aims to convert sentences to semantic vector representations. Most previous works directly use the sentence representations derived from pretr…
Balancing the Causal Effects in Class-Incremental Learning
Junhao Zheng, Ruiyan Wang, Chongzhi Zhang +2
Class-Incremental Learning (CIL) is a practical and challenging problem for achieving general artificial intelligence. Recently, Pre-Trained Models (PTMs) have led to breakthroughs…
Conditional Logical Message Passing Transformer for Complex Query Answering
Chongzhi Zhang, Zhiping Peng, Junhao Zheng +1
Complex Query Answering (CQA) over Knowledge Graphs (KGs) is a challenging task. Given that KGs are usually incomplete, neural models are proposed to solve CQA by performing multi-…
Incremental Sequence Labeling: A Tale of Two Shifts
Shengjie Qiu, Junhao Zheng, Zhen Liu +2
The incremental sequence labeling task involves continuously learning new classes over time while retaining knowledge of the previous ones. Our investigation identifies two signifi…
Can LLMs Learn New Concepts Incrementally without Forgetting?
Junhao Zheng, Shengjie Qiu, Qianli Ma
Large Language Models (LLMs) have achieved remarkable success across various tasks, yet their ability to learn incrementally without forgetting remains underexplored. Incremental l…
Beyond Anti-Forgetting: Multimodal Continual Instruction Tuning with Positive Forward Transfer
Junhao Zheng, Qianli Ma, Zhen Liu +2
Multimodal Continual Instruction Tuning (MCIT) enables Multimodal Large Language Models (MLLMs) to meet continuously emerging requirements without expensive retraining. MCIT faces…