3 citations · 7 across the 10 of their papers we have counts for
4 papers · 1 filter
RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response
Junyu Luo, Xiao Luo, Kaize Ding +3
Supervised fine-tuning (SFT) plays a crucial role in adapting large language models (LLMs) to specific domains or tasks. However, as demonstrated by empirical experiments, the coll…
Political-LLM: Large Language Models in Political Science
Lincan Li, Jiaqi Li, Catherine Chen +44
In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and mis…
AD-LLM: Benchmarking Large Language Models for Anomaly Detection
Tiankai Yang, Yi Nian, Shawn Li +9
Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring. Within natural lang…
LEGO-Learn: Label-Efficient Graph Open-Set Learning
Haoyan Xu, Kay Liu, Zhengtao Yao +4
How can we train graph-based models to recognize unseen classes while keeping labeling costs low? Graph open-set learning (GOL) and out-of-distribution (OOD) detection aim to addre…