20 citations · 38 across the 4 of their papers we have counts for
4 papers
Does Prior Data Matter? Exploring Joint Training in the Context of Few-Shot Class-Incremental Learning
Shiwon Kim, Dongjun Hwang, Sungwon Woo +1
Class-incremental learning (CIL) aims to adapt to continuously emerging new classes while preserving knowledge of previously learned ones. Few-shot class-incremental learning (FSCI…
Krait: A Backdoor Attack Against Graph Prompt Tuning
Ying Song, Rita Singh, Balaji Palanisamy
Graph prompt tuning has emerged as a promising paradigm to effectively transfer general graph knowledge from pre-trained models to various downstream tasks, particularly in few-sho…
Token Prediction as Implicit Classification to Identify LLM-Generated Text
Yutian Chen, Hao Kang, Vivian Zhai +3
This paper introduces a novel approach for identifying the possible large language models (LLMs) involved in text generation. Instead of adding an additional classification layer t…
GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content
Yutian Chen, Hao Kang, Vivian Zhai +3
This paper presents a novel approach for detecting ChatGPT-generated vs. human-written text using language models. To this end, we first collected and released a pre-processed data…