Papers by Liwen Peng

2 papers
RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging (2025.emnlp-main)

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Challenge: Existing models that require task labels or performance trade-offs are susceptible to catastrophic forgetting.
Approach: They propose a representation-aware model merging framework for continual learning without access to historical data.
Outcome: The proposed framework outperforms baselines in knowledge retention and generalization across five NLP tasks and multiple continual learning scenarios.
Emancipating Event Extraction from the Constraints of Long-Tailed Distribution Data Utilizing Large Language Models (2024.lrec-main)

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Challenge: Existing methods for EE depend on manual annotations, which are expensive and scarce.
Approach: They propose to transform the event extraction task into multi-turn dialogues and a novel method for generating high-quality data.
Outcome: The proposed methods significantly improve existing models’ performance with various paradigms and structures, especially on tail types.

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