Papers by Tianshi Che
Exploring the Effectiveness of Multi-Lingual Commonsense Knowledge-Aware Open-Domain Dialogue Response Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies have shown that commonsense knowledge-aware models can improve informativeness while reducing the hallucination issue. |
| Approach: | They propose a task to use commonsense knowledge in other languages to enhance the current dialogue generation by using commonsensical knowledge in different languages. |
| Outcome: | The proposed model improves the current dialogue generation while reducing the hallucination issue. |
Adversarial Attack against Cross-lingual Knowledge Graph Alignment (2021.emnlp-main)
Copied to clipboard
Zeru Zhang, Zijie Zhang, Yang Zhou, Lingfei Wu, Sixing Wu, Xiaoying Han, Dejing Dou, Tianshi Che, Da Yan
| Challenge: | Existing studies on cross-lingual entity alignment under adversarial attacks have not been conducted. |
| Approach: | They propose to use adversarial attack techniques to perturb cross-lingual entity alignment under adversarials. |
| Outcome: | The proposed model hides the attacked entities in dense regions in two KGs, and reduces the gradient vanishing issues in the process of adversarial attacks for further improving the attack effectiveness. |
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization (2023.emnlp-main)
Copied to clipboard
| Challenge: | Prompt tuning of Large Language Models (LLMs) can incur performance degradation or low training efficiency. |
| Approach: | They propose a prompt tuning approach with Adaptive Optimization to enable efficient FL of LLMs. |
| Outcome: | The proposed approach improves performance and efficiency simultaneously and addresses client drift problems on both the device and server sides. |