9 papers
SemStruct: Contextualizing Semantic Embeddings with Structural Information for Schema Matching
Inwon Kang, Kavitha Srinivas, Nandana Mihindukulasooriya +4
Schema matching is a fundamental step in integrating heterogeneous data sources. While Pre-trained Language Models (PLMs) have revolutionized this task by capturing linguistic sema…
Balancing Multi-modal Sensor Learning via Multi-objective Optimization
Heshan Fernando, Quan Xiao, Parikshit Ram +4
Learning-enabled control systems increasingly rely on multiple sensing modalities (e.g., vision, audio, language, etc.) for perception and decision support. A key challenge is that…
Language Model Representations for Efficient Few-Shot Tabular Classification
Inwon Kang, Parikshit Ram, Yi Zhou +2
The Web is a rich source of structured data in the form of tables, from product catalogs and knowledge bases to scientific datasets. However, the heterogeneity of the structure and…
Understanding Forgetting in LLM Supervised Fine-Tuning and Preference Learning -- A Convex Optimization Perspective
Heshan Fernando, Han Shen, Parikshit Ram +4
The post-training of LLMs, which typically consists of the supervised fine-tuning (SFT) stage and the preference learning stage (RLHF or DPO), is crucial to effective and safe LLM…
Reasoning Model Unlearning: Forgetting Traces, Not Just Answers, While Preserving Reasoning Skills
Changsheng Wang, Chongyu Fan, Yihua Zhang +5
Recent advances in large reasoning models (LRMs) have enabled strong chain-of-thought (CoT) generation through test-time computation. While these multi-step reasoning capabilities…
Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-Tuning
Changsheng Wang, Yihua Zhang, Jinghan Jia +6
Machine unlearning offers a promising solution to privacy and safety concerns in large language models (LLMs) by selectively removing targeted knowledge while preserving utility. H…