46 citations · 48 across the 12 of their papers we have counts for
13 papers
Memory-Induced Tool-Drift in LLM Agents
Mahavir Dabas, Jihyun Jeong, Ming Jin +1
LLM agents increasingly combine long-term memory for personalization with tool calling for taking actions. We study a failure of this combination: personality-driven preferences st…
Remembering More, Risking More: Longitudinal Safety Risks in Memory-Equipped LLM Agents
Ahmad Al-Tawaha, Shangding Gu, Peizhi Niu +2
Safety evaluations of memory-equipped LLM agents typically measure within-task safety: whether an agent completes a single scenario safely, often under adversarial conditions such…
Just Enough Shifts: Mitigating Over-Refusal in Aligned Language Models with Targeted Representation Fine-Tuning
Mahavir Dabas, Si Chen, Charles Fleming +2
Safety alignment is crucial for large language models (LLMs) to resist malicious instructions but often results in over-refusals, where benign prompts are unnecessarily rejected, i…
LLM Can be a Dangerous Persuader: Empirical Study of Persuasion Safety in Large Language Models
Minqian Liu, Zhiyang Xu, Xinyi Zhang +8
Recent advancements in Large Language Models (LLMs) have enabled them to approach human-level persuasion capabilities. However, such potential also raises concerns about the safety…
Optimizing Product Provenance Verification using Data Valuation Methods
Raquib Bin Yousuf, Hoang Anh Just, Shengzhe Xu +8
Determining and verifying product provenance remains a critical challenge in global supply chains, particularly as geopolitical conflicts and shifting borders create new incentives…
LLMs Can Plan Only If We Tell Them
Bilgehan Sel, Ruoxi Jia, Ming Jin
Large language models (LLMs) have demonstrated significant capabilities in natural language processing and reasoning, yet their effectiveness in autonomous planning has been under…
Boosting Alignment for Post-Unlearning Text-to-Image Generative Models
Myeongseob Ko, Henry Li, Zhun Wang +6
Large-scale generative models have shown impressive image-generation capabilities, propelled by massive data. However, this often inadvertently leads to the generation of harmful o…
DiPT: Enhancing LLM reasoning through diversified perspective-taking
Hoang Anh Just, Mahavir Dabas, Lifu Huang +2
Existing work on improving language model reasoning typically explores a single solution path, which can be prone to errors. Inspired by perspective-taking in social studies, this…
Data-Centric Human Preference with Rationales for Direct Preference Alignment
Hoang Anh Just, Ming Jin, Anit Sahu +2
Aligning language models with human preferences through reinforcement learning from human feedback is crucial for their safe and effective deployment. The human preference is typic…
Skin-in-the-Game: Decision Making via Multi-Stakeholder Alignment in LLMs
Bilgehan Sel, Priya Shanmugasundaram, Mohammad Kachuee +3
Large Language Models (LLMs) have shown remarkable capabilities in tasks such as summarization, arithmetic reasoning, and question answering. However, they encounter significant ch…
Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization?
Jianfeng He, Runing Yang, Linlin Yu +5
Text summarization, a key natural language generation (NLG) task, is vital in various domains. However, the high cost of inaccurate summaries in risk-critical applications, particu…
Practical Membership Inference Attacks Against Large-Scale Multi-Modal Models: A Pilot Study
Myeongseob Ko, Ming Jin, Chenguang Wang +1
Membership inference attacks (MIAs) aim to infer whether a data point has been used to train a machine learning model. These attacks can be employed to identify potential privacy v…
Environmental Sensing by Wearable Device for Indoor Activity and Location Estimation
Ming Jin, Han Zou, Kevin Weekly +3
We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carr…