Papers by Haiming Wu
Prototype-based Prompt-Instance Interaction with Causal Intervention for Few-shot Event Detection (2024.lrec-main)
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| Challenge: | Few-shot Event Detection (FSED) requires limited labeled data and expensive manual labeling. |
| Approach: | They propose a prototype-based prompt-instance Interaction with causal Intervention model to utilize both prompts and verbalizers and effectively eliminate all biases. |
| Outcome: | The proposed model utilizes both prompts and verbalizers and eliminates all biases on RAMS and ACE datasets. |
Controllable Text Generation with Residual Memory Transformer (2024.findings-acl)
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| Challenge: | Large-scale Causal Language Models (CLMs) have been successful in text generation, but there is still a challenge to control the generation process. |
| Approach: | They propose a non-intrusive, lightweight control plugin to control the generation process of a CLM at arbitrary time steps. |
| Outcome: | The proposed plugin can handle any type of control conditions and cooperate with the base CLM through a residual learning paradigm. |
Reasoning-Oriented and Analogy-Based Methods for Locating and Editing in Zero-Shot Event-Relational Reasoning (2025.coling-main)
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| Challenge: | Existing methods for zero-shot event-relational reasoning require large computational resources and lack interpretability. |
| Approach: | They propose a method for Reasoning-Oriented Locating and Editing which locates and edits key modules of the language model for reasoning about event relations. |
| Outcome: | The proposed method improves interpretability and efficiency with reduced computational cost and achieves SOTA results in zero-shot event-relational reasoning. |
PersonaArena: Dynamic Simulation for Evaluating and Enhancing Persona-Level Role-Playing in Large Language Models (2026.findings-acl)
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| Challenge: | Existing research focuses on character-level settings and static evaluation formats fail to capture the complexity of everyday social interactions. |
| Approach: | They propose a dynamic simulation framework for evaluating and improving persona-level role-playing in large language models (LLMs). |
| Outcome: | The proposed framework leverages user-generated social content to construct a nuanced persona bank and elicits multi-turn, context-rich interactions within simulated social environments. |
Bi-DCSpell: A Bi-directional Detector-Corrector Interactive Framework for Chinese Spelling Check (2024.findings-emnlp)
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| Challenge: | Chinese Spelling Check (CSC) aims to detect and correct potentially misspelled characters in Chinese sentences. |
| Approach: | They propose a bi-directional Detector-Corrector framework for Chinese Spelling Check which mutually enhances the feature representation for detection and correction subtasks. |
| Outcome: | The proposed framework reduces the risk of over-correction and under-corrections while preserving the knowledge learnt from correction. |