115 citations · 369 across the 83 of their papers we have counts for
51 papers · 1 filter
DiscourseFlip: An Oblique Discourse-Level Opinion Manipulation Attack against Black-box Retrieval-Augmented Generation
Yuyang Gong, Miaokun Chen, Jiawei Liu +5
Retrieval-Augmented Generation (RAG) systems are widely deployed and increasingly influential, but their reliance on external corpora exposes new security risks from poisoned retri…
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…
"Excuse me, may I say something..." CoLabScience, A Proactive AI Assistant for Biomedical Discovery and LLM-Expert Collaborations
Yang Wu, Jinhong Yu, Jingwei Xiong +2
The integration of Large Language Models (LLMs) into scientific workflows presents exciting opportunities to accelerate biomedical discovery. However, the reactive nature of LLMs,…
PubMed Reasoner: Dynamic Reasoning-based Retrieval for Evidence-Grounded Biomedical Question Answering
Yiqing Zhang, Xiaozhong Liu, Fabricio Murai
Trustworthy biomedical question answering (QA) systems must not only provide accurate answers but also justify them with current, verifiable evidence. Retrieval-augmented approache…
P2S: Probabilistic Process Supervision for General-Domain Reasoning Question Answering
Wenlin Zhong, Chengyuan Liu, Yiquan Wu +5
While reinforcement learning with verifiable rewards (RLVR) has advanced LLM reasoning in structured domains like mathematics and programming, its application to general-domain rea…
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…
BrailleLLM: Braille Instruction Tuning with Large Language Models for Braille Domain Tasks
Tianyuan Huang, Zepeng Zhu, Hangdi Xing +6
Braille plays a vital role in education and information accessibility for visually impaired individuals. However, Braille information processing faces challenges such as data scarc…
AssoMem: Scalable Memory QA with Multi-Signal Associative Retrieval
Kai Zhang, Xinyuan Zhang, Ejaz Ahmed +11
Accurate recall from large scale memories remains a core challenge for memory augmented AI assistants performing question answering (QA), especially in similarity dense scenarios w…
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…
AppealCase: A Dataset and Benchmark for Civil Case Appeal Scenarios
Yuting Huang, Meitong Guo, Yiquan Wu +6
Recent advances in LegalAI have primarily focused on individual case judgment analysis, often overlooking the critical appellate process within the judicial system. Appeals serve a…
LeCoDe: A Benchmark Dataset for Interactive Legal Consultation Dialogue Evaluation
Weikang Yuan, Kaisong Song, Zhuoren Jiang +6
Legal consultation is essential for safeguarding individual rights and ensuring access to justice, yet remains costly and inaccessible to many individuals due to the shortage of pr…
KEDRec-LM: A Knowledge-distilled Explainable Drug Recommendation Large Language Model
Kai Zhang, Rui Zhu, Shutian Ma +4
Drug discovery is a critical task in biomedical natural language processing (NLP), yet explainable drug discovery remains underexplored. Meanwhile, large language models (LLMs) hav…
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…
Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach
Param Kulkarni, Yingchi Liu, Hao-Ming Fu +8
Achieving a delicate balance between fostering trust in law enforcement and protecting the rights of both officers and civilians continues to emerge as a pressing research and prod…
Topic-FlipRAG: Topic-Orientated Adversarial Opinion Manipulation Attacks to Retrieval-Augmented Generation Models
Yuyang Gong, Zhuo Chen, Jiawei Liu +5
Retrieval-Augmented Generation (RAG) systems based on Large Language Models (LLMs) have become essential for tasks such as question answering and content generation. However, their…
A Speaker Turn-Aware Multi-Task Adversarial Network for Joint User Satisfaction Estimation and Sentiment Analysis
Kaisong Song, Yangyang Kang, Jiawei Liu +3
User Satisfaction Estimation is an important task and increasingly being applied in goal-oriented dialogue systems to estimate whether the user is satisfied with the service. It is…
Science Out of Its Ivory Tower: Improving Accessibility with Reinforcement Learning
Haining Wang, Jason Clark, Hannah McKelvey +5
A vast amount of scholarly work is published daily, yet much of it remains inaccessible to the general public due to dense jargon and complex language. To address this challenge in…
Privacy-preserved LLM Cascade via CoT-enhanced Policy Learning
Kai Zhang, Congchao Wang, Liqian Peng +2
Large Language Models (LLMs) have gained significant attention in on-device applications due to their remarkable performance across real-world tasks. However, on-device LLMs often…
Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models
Zhuo Chen, Jiawei Liu, Haotan Liu +4
Retrieval-Augmented Generation (RAG) is applied to solve hallucination problems and real-time constraints of large language models, but it also induces vulnerabilities against retr…
Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning
Yang Wu, Chenghao Wang, Ece Gumusel +1
The integration of generative Large Language Models (LLMs) into various applications, including the legal domain, has been accelerated by their expansive and versatile nature. Howe…
Enhance Robustness of Language Models Against Variation Attack through Graph Integration
Zi Xiong, Lizhi Qing, Yangyang Kang +5
The widespread use of pre-trained language models (PLMs) in natural language processing (NLP) has greatly improved performance outcomes. However, these models' vulnerability to adv…
From Model-centered to Human-Centered: Revision Distance as a Metric for Text Evaluation in LLMs-based Applications
Yongqiang Ma, Lizhi Qing, Jiawei Liu +5
Evaluating large language models (LLMs) is fundamental, particularly in the context of practical applications. Conventional evaluation methods, typically designed primarily for LLM…
Personalized LLM Response Generation with Parameterized Memory Injection
Kai Zhang, Yejin Kim, Xiaozhong Liu
Large Language Models (LLMs) have exhibited remarkable proficiency in comprehending and generating natural language. On the other hand, personalized LLM response generation holds t…