3 papers
cs.CL2025
STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework
Wenhao Liu, Zhenyi Lu, Xinyu Hu +13
High-quality math datasets are crucial for advancing the reasoning abilities of large language models (LLMs). However, existing datasets often suffer from three key issues: outdate…
cs.LG2025
LoTA-QAF: Lossless Ternary Adaptation for Quantization-Aware Fine-Tuning
Junyu Chen, Junzhuo Li, Zhen Peng +4
Quantization and fine-tuning are crucial for deploying large language models (LLMs) on resource-constrained edge devices. However, fine-tuning quantized models presents significant…
cs.IR2025
G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation
Yuhan Li, Xinni Zhang, Linhao Luo +4
Explainable recommendation has demonstrated significant advantages in informing users about the logic behind recommendations, thereby increasing system transparency, effectiveness,…