Papers by Rujing Yao
Active Domain Knowledge Acquisition with 100-Dollar Budget: Enhancing LLMs via Cost-Efficient, Expert-Involved Interaction in Sensitive Domains (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated an impressive level of general knowledge, but often struggle in highly specialized domains due to the lack of expert knowledge. |
| Approach: | They propose a framework to actively engage domain experts within a fixed budget to enhance domain-specific LLMs. |
| Outcome: | The proposed framework improves LLMs in highly specialized domains while adhering to budget constraints. |
Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoning (2025.naacl-long)
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| Challenge: | Existing approaches to large language models focus on semantic similarity, neglecting the intricate logical structures and reasoning essential for addressing complex legal issues. |
| Approach: | They propose a Logical-Semantic Integration Model (LSIM) that bridges semantic and logical coherence and a supervised framework that integrates semantic features with in-context learning. |
| Outcome: | The proposed framework significantly improves accuracy and reliability on a real-world legal QA dataset. |
From Individual Excellence to Collective Sustainability: Seeking Strategic Equilibrium in Proactive Multi-Agent Teams (2026.findings-acl)
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| Challenge: | a team of proactive agents suffer from a greedy optimization for immediate task accuracy . a new approach to improve team collaboration is based on the opportunity cost . |
| Approach: | They propose a game-theoretic proactive multi-agent reinforcement learning framework to solve this imbalance . they use a Positive-Unlabeled scorer to anchor intervention quality under sparse supervision . |
| Outcome: | The proposed framework maintains high performance while preventing experts from over-developing. |
Beyond Local vs. External: A Game-Theoretic Framework for Trustworthy Knowledge Acquisition (2026.findings-acl)
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| Challenge: | Cloud-hosted Large Language Models (LLMs) offer unmatched reasoning capabilities and dynamic knowledge, yet submitting raw queries to these services can expose sensitive user intent. |
| Approach: | They propose a framework that formulates the trade-off between knowledge utility and privacy as a strategic game. |
| Outcome: | The proposed framework reduces intent leakage while maintaining high-fidelity answer quality. |