7 papers
LLMs Can Unlearn Refusal with Only 1,000 Benign Samples
Yangyang Guo, Ziwei Xu, Si Liu +2
This study reveals a previously unexplored vulnerability in the safety alignment of Large Language Models (LLMs). Existing aligned LLMs predominantly respond to unsafe queries with…
A Closer Look at Deep Learning Methods on Tabular Datasets
Han-Jia Ye, Si-Yang Liu, Hao-Run Cai +2
Tabular data is prevalent across diverse domains in machine learning. With the rapid progress of deep tabular prediction methods, especially pretrained (foundation) models, there i…
KV Cache Compression for Inference Efficiency in LLMs: A Review
Yanyu Liu, Jingying Fu, Sixiang Liu +4
Withtherapid advancement of large language models (LLMs), the context length for inference has been continuously increasing, leading to an exponential growth in the demand for Key-…
A Closer Look at TabPFN v2: Understanding Its Strengths and Extending Its Capabilities
Han-Jia Ye, Si-Yang Liu, Wei-Lun Chao
Tabular datasets are inherently heterogeneous, presenting significant challenges for developing pre-trained foundation models. The recently introduced transformer-based Tabular Pri…
Make Still Further Progress: Chain of Thoughts for Tabular Data Leaderboard
Si-Yang Liu, Qile Zhou, Han-Jia Ye
Tabular data, a fundamental data format in machine learning, is predominantly utilized in competitions and real-world applications. The performance of tabular models--such as gradi…
Representation Learning for Tabular Data: A Comprehensive Survey
Jun-Peng Jiang, Si-Yang Liu, Hao-Run Cai +2
Tabular data, structured as rows and columns, is among the most prevalent data types in machine learning classification and regression applications. Models for learning from tabula…