70 citations · 439 across the 63 of their papers we have counts for
8 papers · 2 filters
How to Synthesize Text Data without Model Collapse?
Xuekai Zhu, Daixuan Cheng, Hengli Li +7
Model collapse in synthetic data indicates that iterative training on self-generated data leads to a gradual decline in performance. With the proliferation of AI models, synthetic…
Scalable Efficient Training of Large Language Models with Low-dimensional Projected Attention
Xingtai Lv, Ning Ding, Kaiyan Zhang +3
Improving the effectiveness and efficiency of large language models (LLMs) simultaneously is a critical yet challenging research goal. In this paper, we find that low-rank pre-trai…
Fast and Slow Generating: An Empirical Study on Large and Small Language Models Collaborative Decoding
Kaiyan Zhang, Jianyu Wang, Ning Ding +4
Large Language Models (LLMs) exhibit impressive capabilities across various applications but encounter substantial challenges such as high inference latency, considerable training…
The Right Time Matters: Data Arrangement Affects Zero-Shot Generalization in Instruction Tuning
Bingxiang He, Ning Ding, Cheng Qian +10
Understanding alignment techniques begins with comprehending zero-shot generalization brought by instruction tuning, but little of the mechanism has been understood. Existing work…
UltraMedical: Building Specialized Generalists in Biomedicine
Kaiyan Zhang, Sihang Zeng, Ermo Hua +11
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains and are moving towards more specialized areas. Recent advanced proprietary models such…
MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies
Shengding Hu, Yuge Tu, Xu Han +22
The burgeoning interest in developing Large Language Models (LLMs) with up to trillion parameters has been met with concerns regarding resource efficiency and practical expense, pa…
Mastering Text, Code and Math Simultaneously via Fusing Highly Specialized Language Models
Ning Ding, Yulin Chen, Ganqu Cui +6
Underlying data distributions of natural language, programming code, and mathematical symbols vary vastly, presenting a complex challenge for large language models (LLMs) that stri…
UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset
Haoyu Wang, Shuo Wang, Yukun Yan +9
Open-source large language models (LLMs) have gained significant strength across diverse fields. Nevertheless, the majority of studies primarily concentrate on English, with only l…