Papers by Hongli Zhan

4 papers
Evaluating Subjective Cognitive Appraisals of Emotions from Large Language Models (2023.findings-emnlp)

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Challenge: Existing work on automatic prediction of cognitive appraisals has focused on physiological aspects of emotions.
Approach: They present a dataset that assesses 24 appraisal dimensions across 241 Reddit posts . they find that open-source models fail to automatically assess and explain cognitive appraisals .
Outcome: The proposed dataset assesses 24 appraisal dimensions across 241 reddit posts.
Unsupervised Extractive Summarization of Emotion Triggers (2023.acl-long)

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Challenge: Recent approaches trained supervised models to detect emotions and explain emotion triggers via abstractive summarization, but this can block necessary responses.
Approach: They propose to augment an abstractive dataset with extractive triggers and develop unsupervised models that can jointly detect emotions and summarize their triggers.
Outcome: The proposed model outperforms existing models and is based on a COVID-19 crisis dataset.
PwnGPT: Automatic Exploit Generation Based on Large Language Models (2025.acl-long)

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Challenge: Automated exploit generation (AEG) is the automatic discovery and exploitation of vulnerabilities against unknown targets.
Approach: They propose an automatic exploit generation framework that automatically solves pwn challenges by using large language models.
Outcome: The proposed framework improves the completion rate of exploits on the openAI o1-preview model and the GPT-4o model.
Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts (2022.emnlp-main)

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Challenge: Large-scale crises such as the COVID-19 pandemic cause emotional turmoil worldwide.
Approach: They propose a method to jointly detect emotions and summarize emotion triggers in social media posts related to COVID-19.
Outcome: The proposed method can detect emotions and summarize emotions in long social media posts.

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