4 papers
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions
Zhiyin Yu, Yuchen Mou, Juncheng Yan +17
Reinforcement learning (RL) has emerged as a powerful post-training paradigm for enhancing the reasoning capabilities of large language models (LLMs). However, reinforcement learni…
FRESCO: Benchmarking and Optimizing Re-rankers for Evolving Semantic Conflict in Retrieval-Augmented Generation
Sohyun An, Hayeon Lee, Shuibenyang Yuan +4
Retrieval-Augmented Generation (RAG) is a key approach to mitigating the temporal staleness of large language models (LLMs) by grounding responses in up-to-date evidence. Within th…
Preference-driven Knowledge Distillation for Few-shot Node Classification
Xing Wei, Chunchun Chen, Rui Fan +3
Graph neural networks (GNNs) can efficiently process text-attributed graphs (TAGs) due to their message-passing mechanisms, but their training heavily relies on the human-annotated…
Bootstrap Deep Spectral Clustering with Optimal Transport
Wengang Guo, Wei Ye, Chunchun Chen +4
Spectral clustering is a leading clustering method. Two of its major shortcomings are the disjoint optimization process and the limited representation capacity. To address these is…