4 papers · 1 filter
Making the Most of Limited Data: Score-Aware Training for Text-to-Music Generation
Yun-Chen Cheng, Tzu-Hung Huang, Chih-Pin Tan
State-of-the-art text-to-music generation systems rely on massive proprietary datasets and industrial-scale compute, making it impossible to disentangle architectural contributions…
Score Decoder: A Unified View of Unsupervised Inference-Time Scaling for Hallucination Mitigation
Yun-Chen Cheng, Che-Yu Lin, Cheng-Lin Yang
Large language models hallucinate even when the answer lies within their parameters. While inference-time scaling can surface this latent knowledge, the most effective methods requ…
TransMamba: A Sequence-Level Hybrid Transformer-Mamba Language Model
Yixing Li, Ruobing Xie, Zhen Yang +8
Transformers are the cornerstone of modern large language models, but their quadratic computational complexity limits efficiency in long-sequence processing. Recent advancements in…
Scaling Laws for Floating Point Quantization Training
Xingwu Sun, Shuaipeng Li, Ruobing Xie +13
Low-precision training is considered an effective strategy for reducing both training and downstream inference costs. Previous scaling laws for precision mainly focus on integer qu…