Papers by Zhonghua Zheng
Thoughts to Target: Enhance Planning for Target-driven Conversation (2024.emnlp-main)
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| Challenge: | Empirical results demonstrate that our method significantly improves the planning ability of LLMs, especially in target-driven conversations. |
| Approach: | They propose a two-stage framework to improve the LLMs’ capability in planning conversations towards designated targets by distilling natural language plans from a target-driven conversation corpus and generating new plans with demonstration-guided in-context learning. |
| Outcome: | The proposed framework improves the ability of conversational models to plan towards designated targets and can be used to build extensive conversational AI. |
Self-chats from Large Language Models Make Small Emotional Support Chatbot Better (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown strong generalization abilities to excel in various tasks, including emotion support conversations. |
| Approach: | They propose an iterative expansion framework to prompt large teacher model to curate an expansive emotion support dialogue dataset. |
| Outcome: | The proposed model outperforms the teacher model in some cases . the proposed model is based on an iterative expansion framework and is available on github.com/pandazzh2020/ExTES. |