7 papers
PriFT: Prior-Support Guided Supervised Fine-Tuning
Ke Wang, Shuangqi Li, Mathieu Salzmann +1
Supervised fine-tuning (SFT) is an efficient approach for downstream task adaptation and often serves as the initialization stage for reinforcement learning (RL), but it can show w…
TabEmb: Joint Semantic-Structure Embedding for Table Annotation
Ehsan Hoseinzade, Ke Wang, Anandharaju Durai Raju
Table annotation is crucial for making web and enterprise tables usable in downstream NLP applications. Unlike textual data where learning semantically rich token or sentence embed…
HalluSAE: Detecting Hallucinations in Large Language Models via Sparse Auto-Encoders
Boshui Chen, Zhaoxin Fan, Ke Wang +5
Large Language Models (LLMs) are powerful and widely adopted, but their practical impact is limited by the well-known hallucination phenomenon. While recent hallucination detection…
MEMOIR: Lifelong Model Editing with Minimal Overwrite and Informed Retention for LLMs
Ke Wang, Yiming Qin, Nikolaos Dimitriadis +2
Language models deployed in real-world systems often require post-hoc updates to incorporate new or corrected knowledge. However, editing such models efficiently and reliably-witho…
Semantic Document Derendering: SVG Reconstruction via Vision-Language Modeling
Adam Hazimeh, Ke Wang, Mark Collier +3
Multimedia documents such as slide presentations and posters are designed to be interactive and easy to modify. Yet, they are often distributed in a static raster format, which lim…
Single-Input Multi-Output Model Merging: Leveraging Foundation Models for Dense Multi-Task Learning
Juan Garcia Giraldo, Nikolaos Dimitriadis, Ke Wang +1
Model merging is a flexible and computationally tractable approach to merge single-task checkpoints into a multi-task model. Prior work has solely focused on constrained multi-task…