46 citations · 48 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…