Papers by Shi-ang Qi
PepRec: Progressive Enhancement of Prompting for Recommendation (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have been gaining in-depth performance in natural language processing domains. |
| Approach: | They propose a training-free prompting framework that captures knowledge from content-based filtering and collaborative filtering to boost recommendation performance with LLMs. |
| Outcome: | The proposed framework outperforms traditional deep learning recommendation models and prompt-based recommendation systems on two real-world datasets. |
ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis (2023.findings-acl)
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Yakun Yu, Mingjun Zhao, Shi-ang Qi, Feiran Sun, Baoxun Wang, Weidong Guo, Xiaoli Wang, Lei Yang, Di Niu
| Challenge: | Existing methods for multimodal sentiment analysis focus on general knowledge, which is inadequate to identify specific sentiments across modalities. |
| Approach: | They propose a method where specific-knowledge representations for each modality can be learned together with general knowledge representations via knowledge injection based on an adapter architecture. |
| Outcome: | The proposed method outperforms all prior methods on three popular benchmarks on multimodal sentiment analysis metrics. |