Papers by TengQi Ye
Prompt Optimization for Relation Extraction using Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing prompt-based methods rely heavily on large-scale annotated datasets limiting their applicability in domain-specific and low-resource scenarios. |
| Approach: | They propose a reinforcement learning-based automated prompt optimization framework for domain relation extraction that optimizes prompt quality through interaction with a black-box LLM. |
| Outcome: | The proposed framework outperforms existing prompt-based methods and supervised baselines on multiple extraction datasets across medical, financial, legal, and news domains. |