Papers by Kezhen Chen
NICE: Neural Image Commenting with Empathy (2021.findings-emnlp)
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Kezhen Chen, Qiuyuan Huang, Daniel McDuff, Xiang Gao, Hamid Palangi, Jianfeng Wang, Kenneth Forbus, Jianfeng Gao
| Challenge: | Emotion and empathy are examples of human qualities lacking in many human-machine interactions. |
| Approach: | They propose to generate images with human-generated comments with enhanced emotion and empathy while minimizing inappropriate or offensive outputs. |
| Outcome: | The proposed model generates more human-like and engaging image comments on two images with human-generated comments and human annotations while minimizing inappropriate or offensive outputs. |
MoLA: MoE LoRA with Layer-wise Expert Allocation (2025.findings-naacl)
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Chongyang Gao, Kezhen Chen, Jinmeng Rao, Ruibo Liu, Baochen Sun, Yawen Zhang, Daiyi Peng, Xiaoyuan Guo, Vs Subrahmanian
| Challenge: | Recent efforts to integrate low-rank adaptation (LoRA) with the Mixture-of-Experts (MoE) have achieved performance comparable to full-parameter fine-tuning by tuning much fewer parameters. |
| Approach: | They propose a parameter-efficient MoE method for low-rank adaptation with the Mixture-of-Experts (MoE) they use layers of LoRA experts to allocate more LoRA expert to middle layers . |
| Outcome: | The proposed method outperforms baseline models on six well-known NLP and commonsense QA benchmarks on LLAMA-2, Mistral, and Gemma. |