23 citations · 64 across the 22 of their papers we have counts for
22 papers
Learning to Unlearn: Machine Unlearning via Learning the Unlearning Behaviors
Hang Zhang, Kaifeng Zhang, Yixiao Ma +3
Various machine unlearning techniques have been developed in response to privacy legislation requirements, enabling individuals to exercise their legal right to have their data $D_…
Visual Exclusivity Attacks: Automatic Multimodal Red Teaming via Agentic Planning
Yunbei Zhang, Yingqiang Ge, Weijie Xu +3
Current multimodal red teaming treats images as wrappers for malicious payloads via typography or adversarial noise. These attacks are structurally brittle, as standard defenses ne…
Distractor Injection Attacks on Large Reasoning Models: Characterization and Defense
Zhehao Zhang, Weijie Xu, Shixian Cui +1
Recent advances in large reasoning models (LRMs) have enabled remarkable performance on complex tasks such as mathematics and coding by generating long Chain-of-Thought (CoT) trace…
The Personalization Trap: How User Memory Alters Emotional Reasoning in LLMs
Xi Fang, Weijie Xu, Yuchong Zhang +3
When an AI assistant remembers that Sarah is a single mother working two jobs, does it interpret her stress differently than if she were a wealthy executive? As personalized AI sys…
Quantifying Fairness in LLMs Beyond Tokens: A Semantic and Statistical Perspective
Weijie Xu, Yiwen Wang, Chi Xue +4
Large Language Models (LLMs) often generate responses with inherent biases, undermining their reliability in real-world applications. Existing evaluation methods often overlook bia…
SATA-BENCH: Select All That Apply Benchmark for Multiple Choice Questions
Weijie Xu, Shixian Cui, Xi Fang +3
Large language models (LLMs) are increasingly evaluated on single-answer multiple-choice tasks, yet many real-world problems require identifying all correct answers from a set of o…
Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems
Jiaxi Yang, Mengqi Zhang, Yiqiao Jin +8
Large Language Model-based Multi-Agent Systems (MASs) have emerged as a powerful paradigm for tackling complex tasks through collaborative intelligence. However, the topology of th…
FalseReject: A Resource for Improving Contextual Safety and Mitigating Over-Refusals in LLMs via Structured Reasoning
Zhehao Zhang, Weijie Xu, Fanyou Wu +1
Safety alignment approaches in large language models (LLMs) often lead to the over-refusal of benign queries, significantly diminishing their utility in sensitive scenarios. To add…
Neural Topic Modeling with Large Language Models in the Loop
Xiaohao Yang, He Zhao, Weijie Xu +4
Topic modeling is a fundamental task in natural language processing, allowing the discovery of latent thematic structures in text corpora. While Large Language Models (LLMs) have d…
HR-Agent: A Task-Oriented Dialogue (TOD) LLM Agent Tailored for HR Applications
Weijie Xu, Jay Desai, Fanyou Wu +2
Recent LLM (Large Language Models) advancements benefit many fields such as education and finance, but HR has hundreds of repetitive processes, such as access requests, medical cla…
Mitigating Selection Bias with Node Pruning and Auxiliary Options
Hyeong Kyu Choi, Weijie Xu, Chi Xue +2
Large language models (LLMs) often exhibit systematic preferences for certain answer choices when responding to multiple-choice questions-a behavior known as selection bias. This b…
Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentation
Fanyou Wu, Weijie Xu, Chandan K. Reddy +1
In this study, we tackle the challenge of inadequate and costly training data that has hindered the development of conversational question answering (ConvQA) systems. Enterprises h…
PHAnToM: Persona-based Prompting Has An Effect on Theory-of-Mind Reasoning in Large Language Models
Fiona Anting Tan, Gerard Christopher Yeo, Kokil Jaidka +6
The use of LLMs in natural language reasoning has shown mixed results, sometimes rivaling or even surpassing human performance in simpler classification tasks while struggling with…
Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey
Xi Fang, Weijie Xu, Fiona Anting Tan +7
Recent breakthroughs in large language modeling have facilitated rigorous exploration of their application in diverse tasks related to tabular data modeling, such as prediction, ta…
HR-MultiWOZ: A Task Oriented Dialogue (TOD) Dataset for HR LLM Agent
Weijie Xu, Zicheng Huang, Wenxiang Hu +5
Recent advancements in Large Language Models (LLMs) have been reshaping Natural Language Processing (NLP) task in several domains. Their use in the field of Human Resources (HR) ha…
Sequence-Level Certainty Reduces Hallucination In Knowledge-Grounded Dialogue Generation
Yixin Wan, Fanyou Wu, Weijie Xu +1
In this work, we propose sequence-level certainty as a common theme over hallucination in Knowledge Grounded Dialogue Generation (KGDG). We explore the correlation between the leve…
DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM
Weijie Xu, Wenxiang Hu, Fanyou Wu +1
In the burgeoning field of natural language processing (NLP), Neural Topic Models (NTMs) , Large Language Models (LLMs) and Diffusion model have emerged as areas of significant res…
Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning
Tamas Madl, Weijie Xu, Olivia Choudhury +1
The availability of large amounts of informative data is crucial for successful machine learning. However, in domains with sensitive information, the release of high-utility data w…
S2vNTM: Semi-supervised vMF Neural Topic Modeling
Weijie Xu, Jay Desai, Srinivasan Sengamedu +2
Language model based methods are powerful techniques for text classification. However, the models have several shortcomings. (1) It is difficult to integrate human knowledge such a…
KDSTM: Neural Semi-supervised Topic Modeling with Knowledge Distillation
Weijie Xu, Xiaoyu Jiang, Jay Desai +3
In text classification tasks, fine tuning pretrained language models like BERT and GPT-3 yields competitive accuracy; however, both methods require pretraining on large text datase…
vONTSS: vMF based semi-supervised neural topic modeling with optimal transport
Weijie Xu, Xiaoyu Jiang, Srinivasan H. Sengamedu +2
Recently, Neural Topic Models (NTM), inspired by variational autoencoders, have attracted a lot of research interest; however, these methods have limited applications in the real w…
FFPDG: Fast, Fair and Private Data Generation
Weijie Xu, Jinjin Zhao, Francis Iannacci +1
Generative modeling has been used frequently in synthetic data generation. Fairness and privacy are two big concerns for synthetic data. Although Recent GAN [\cite{goodfellow2014ge…