Papers by Fengjun Pan
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. |
Towards the TopMost: A Topic Modeling System Toolkit (2024.acl-demos)
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| Challenge: | Current topic models adopt totally different datasets, implementations, and evaluations, hindering their research progress and applications. |
| Approach: | They propose a Topic Modeling System Toolkit that covers a broader spectrum of topic modeling scenarios with their complete lifecycles. |
| Outcome: | The proposed toolkit covers a broader spectrum of topic modeling scenarios with their complete lifecycles, including datasets, preprocessing, models, training, and evaluations. |
Are LLMs Good Zero-Shot Fallacy Classifiers? (2024.emnlp-main)
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| Challenge: | Existing fallacy classifiers lack sufficient labeled data for training, limiting their out-of-distribution (OOD) generalization abilities. |
| Approach: | They propose to use Large Language Models (LLMs) for zero-shot fallacy classification. |
| Outcome: | The proposed schemes outperform existing classifiers in OOD inference scenarios and opendomain tasks. |