Papers by Congcong Liu

2 papers
Rehearsal-free Continual Language Learning via Efficient Parameter Isolation (2023.acl-long)

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Challenge: Existing methods for learning continual tasks do not cache history data, which makes the problem more challenging.
Approach: They propose a method that allocates a small portion of private parameters and learns them with a shared pre-trained model.
Outcome: The proposed method is comparable to existing methods and comparable to those using historical data.
HierDiffuse: Progressive Diffusion for Robust Interest Fusion in CTR Prediction (2025.emnlp-industry)

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Challenge: Existing approaches fuse long-term behavioral profiles and short-term interactions, suffering from representational misalignment and noise in transient signals.
Approach: They propose a framework that redefines interest fusion as a hierarchical denoising process through diffusion models.
Outcome: The proposed framework redefines interest fusion as a hierarchical denoising process through diffusion models.

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