Papers by Meihuizi Jia
E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service (2022.lrec-1)
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Meihuizi Jia, Ruixue Liu, Peiying Wang, Yang Song, Zexi Xi, Haobin Li, Xin Shen, Meng Chen, Jinhui Pang, Xiaodong He
| Challenge: | Recent research has focused on developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations. |
| Approach: | They construct an authentic Chinese dialogue dataset consisting of over 25k dialogues and 770k utterances, which contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders. |
| Outcome: | The proposed dataset contains user profile, product knowledge base, and multiple sequential real conversations between users and recommenders. |
Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning (2024.emnlp-main)
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| Challenge: | In-context learning has shown high efficacy in several NLP tasks, especially in few-shot settings. |
| Approach: | They propose a backdoor attack method that poisons demonstration examples and poisons the demonstration context, preserving the model's generality. |
| Outcome: | The proposed method can make models behave in alignment with predefined intentions without fine-tuning the model. |
Modularized Interaction Network for Named Entity Recognition (2021.acl-long)
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| Challenge: | Named Entity Recognition (NER) models focus on word-level information, while segment-based models focus only on word level information. |
| Approach: | They propose a Modularized Interaction Network (MIN) model which utilizes both word-level information and segment-level dependencies. |
| Outcome: | The proposed model outperforms the current state-of-the-art models on three NER benchmark datasets. |
Separation and Fusion: A Novel Multiple Token Linking Model for Event Argument Extraction (2024.naacl-long)
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Jing Xu, Dandan Song, Siu Hui, Zhijing Wu, Meihuizi Jia, Hao Wang, Yanru Zhou, Changzhi Zhou, Ziyi Yang
| Challenge: | Existing methods for event argument extraction (EAE) lack cross-event information and require longer role sequences . et al. (2017): outperforms state-of-the-art methods for EE. |
| Approach: | They propose a separation-and-fusion paradigm to separate the acquisition of cross-event information and fuse it into the argument extraction of a target event. |
| Outcome: | The proposed model outperforms the state-of-the-art models on four widely used datasets. |
Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning (2024.findings-naacl)
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| Challenge: | Existing methods for parameter-efficient fine-tuning (PEFT) are not effective for weight-poisoning backdoor attacks. |
| Approach: | They propose a parameter-efficient fine-tuning (PEFT) method that updates only a limited set of model parameters and provides a robust defense against weight-poisoning backdoor attacks. |
| Outcome: | The proposed method identifies poisoned samples through confidence and is robust against weight-poisoning backdoor attacks. |
Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge Distillation (2025.findings-acl)
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| Challenge: | Parameter-efficient fine-tuning (PEFT) can bridge the gap between large language models and downstream tasks, but is vulnerable to malicious attacks. |
| Approach: | They propose a weak-to-strong unlearning algorithm based on feature alignment knowledge distillation to defend against backdoor attacks . they first train a small-scale language model through full-parameter fine-tuning to serve as the clean teacher model and then guide the large-scale poisoned student model in unlearning the backdoor. |
| Outcome: | The proposed method can unlearn backdoor features without compromising model performance. |