5 papers
Beyond Local vs. External: A Game-Theoretic Framework for Trustworthy Knowledge Acquisition
Rujing Yao, Yufei Shi, Yang Wu +5
Cloud-hosted Large Language Models (LLMs) offer unmatched reasoning capabilities and dynamic knowledge, yet submitting raw queries to these external services risks exposing sensiti…
Teaching According to Students' Aptitude: Personalized Mathematics Tutoring via Persona-, Memory-, and Forgetting-Aware LLMs
Yang Wu, Rujing Yao, Tong Zhang +4
Large Language Models (LLMs) are increasingly integrated into intelligent tutoring systems to provide human-like and adaptive instruction. However, most existing approaches fail to…
Active Domain Knowledge Acquisition with 100-Dollar Budget: Enhancing LLMs via Cost-Efficient, Expert-Involved Interaction in Sensitive Domains
Yang Wu, Raha Moraffah, Rujing Yao +3
Large Language Models (LLMs) have demonstrated an impressive level of general knowledge. However, they often struggle in highly specialized and cost-sensitive domains such as drug…
Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoning
Rujing Yao, Yang Wu, Chenghao Wang +3
Large Language Models (LLMs) have achieved impressive results across numerous domains, yet they experience notable deficiencies in legal question-answering tasks. LLMs often genera…
Intelligent Legal Assistant: An Interactive Clarification System for Legal Question Answering
Rujing Yao, Yiquan Wu, Tong Zhang +7
The rise of large language models has opened new avenues for users seeking legal advice. However, users often lack professional legal knowledge, which can lead to questions that om…