3 citations · 9 across the 26 of their papers we have counts for
6 papers · 1 filter
Leveraging Open Knowledge for Advancing Task Expertise in Large Language Models
Yuncheng Yang, Yulei Qin, Tong Wu +9
The cultivation of expertise for large language models (LLMs) to solve tasks of specific areas often requires special-purpose tuning with calibrated behaviors on the expected stabl…
Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models
Yulei Qin, Yuncheng Yang, Pengcheng Guo +7
Instruction tuning plays a critical role in aligning large language models (LLMs) with human preference. Despite the vast amount of open instruction datasets, naively training a LL…
IceFormer: Accelerated Inference with Long-Sequence Transformers on CPUs
Yuzhen Mao, Martin Ester, Ke Li
One limitation of existing Transformer-based models is that they cannot handle very long sequences as input since their self-attention operations exhibit quadratic time and space c…
Genetic Auto-prompt Learning for Pre-trained Code Intelligence Language Models
Chengzhe Feng, Yanan Sun, Ke Li +3
As Pre-trained Language Models (PLMs), a popular approach for code intelligence, continue to grow in size, the computational cost of their usage has become prohibitively expensive.…
RESTORE: Towards Feature Shift for Vision-Language Prompt Learning
Yuncheng Yang, Chuyan Zhang, Zuopeng Yang +6
Prompt learning is effective for fine-tuning foundation models to improve their generalization across a variety of downstream tasks. However, the prompts that are independently opt…
Sinkhorn Distance Minimization for Knowledge Distillation
Xiao Cui, Yulei Qin, Yuting Gao +7
Knowledge distillation (KD) has been widely adopted to compress large language models (LLMs). Existing KD methods investigate various divergence measures including the Kullback-Lei…