EFPC: Towards Efficient and Flexible Prompt Compression

no code implementations • 11 Mar 2025 • Yun-Hao Cao, Yangsong Wang, Shuzheng Hao, Zhenxing Li, Chengjun Zhan, Sichao Liu, Yi-Qi Hu

Compared to the state-of-the-art method LLMLingua-2, EFPC achieves a 4. 8% relative improvement in F1-score with 1% additional data at a 4x compression rate, and an 11. 4% gain with 10% additional data on the LongBench single-doc QA benchmark.

Seed-TTS: A Family of High-Quality Versatile Speech Generation Models

2 code implementations • 4 Jun 2024 • Philip Anastassiou, Jiawei Chen, Jitong Chen, Yuanzhe Chen, Zhuo Chen, Ziyi Chen, Jian Cong, Lelai Deng, Chuang Ding, Lu Gao, Mingqing Gong, Peisong Huang, Qingqing Huang, Zhiying Huang, YuanYuan Huo, Dongya Jia, ChuMin Li, Feiya Li, Hui Li, Jiaxin Li, Xiaoyang Li, Xingxing Li, Lin Liu, Shouda Liu, Sichao Liu, Xudong Liu, Yuchen Liu, Zhengxi Liu, Lu Lu, Junjie Pan, Xin Wang, Yuping Wang, Yuxuan Wang, Zhen Wei, Jian Wu, Chao Yao, Yifeng Yang, YuanHao Yi, Junteng Zhang, Qidi Zhang, Shuo Zhang, Wenjie Zhang, Yang Zhang, Zilin Zhao, Dejian Zhong, Xiaobin Zhuang

Seed-TTS offers superior controllability over various speech attributes such as emotion and is capable of generating highly expressive and diverse speech for speakers in the wild.

In-Context Learning Language Modelling +2

Rethinking Optimization and Architecture for Tiny Language Models

1 code implementation • 5 Feb 2024 • Yehui Tang, Kai Han, Fangcheng Liu, Yunsheng Ni, Yuchuan Tian, Zheyuan Bai, Yi-Qi Hu, Sichao Liu, Shangling Jui, Yunhe Wang

Several design formulas are empirically proved especially effective for tiny language models, including tokenizer compression, architecture tweaking, parameter inheritance and multiple-round training.

Language Modelling