Papers by Yangkun Wang
Direct Prompt Optimization with Continuous Representations (2025.acl-long)
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| Challenge: | Existing methods for prompt optimization for language models lack extensibility and search space. |
| Approach: | They propose a method that integrates greedy strategies into optimization with continuous representations to address instability caused by rounding. |
| Outcome: | The proposed approach can improve prompt optimization performance on text classification and attack tasks, as well as models, including GPT-2, OPT, Vicuna, and LLaMA-2. |
S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation (2022.aacl-main)
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| Challenge: | Emotion recognition in conversation (ERC) is a task arousing increasing interest in many fields. |
| Approach: | They propose a novel GNN-based ERC model that captures speaker and position information. |
| Outcome: | The proposed model captures speaker and position-aware conversation structure information. |
Towards Few-shot Entity Recognition in Document Images: A Graph Neural Network Approach Robust to Image Manipulation (2024.lrec-main)
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| Challenge: | Existing methods for named entity recognition from document images are limited in few-shot settings. |
| Approach: | They propose a framework which leverages the topological adjacency relationship among tokens by learning layout information with graph neural networks. |
| Outcome: | The proposed framework outperforms baselines under different few-shot settings and shows better performance to image manipulations. |
ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation (2023.findings-emnlp)
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| Challenge: | toxicity detection has been largely based on social media content, leaving the unique challenges inherent to real-world user-AI interactions insufficiently explored. |
| Approach: | They propose a benchmark to detect toxicity in real-world user-AI conversations . they compare existing models with social media content to find toxicity . |
| Outcome: | The proposed benchmark reveals that existing models fail to recognize toxicity in real-world user-AI conversations. |