6 citations · 12 across the 5 of their papers we have counts for
5 papers
MathScale: Scaling Instruction Tuning for Mathematical Reasoning
Zhengyang Tang, Xingxing Zhang, Benyou Wang +1
Large language models (LLMs) have demonstrated remarkable capabilities in problem-solving. However, their proficiency in solving mathematical problems remains inadequate. We propos…
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models
Haoran Li, Qingxiu Dong, Zhengyang Tang +17
We introduce Generalized Instruction Tuning (called GLAN), a general and scalable method for instruction tuning of Large Language Models (LLMs). Unlike prior work that relies on se…
Perturbing Masses: A Study of Centered Co-Circular Configurations in Power-Law n-Body Problems
Zhengyang Tang, Shuqiang Zhu
This research investigates centered co-circular central configurations in the general power-law potential -body problem. Firstly, there are no such configurations when all masse…
Modular Retrieval for Generalization and Interpretation
Juhao Liang, Chen Zhang, Zhengyang Tang +3
New retrieval tasks have always been emerging, thus urging the development of new retrieval models. However, instantiating a retrieval model for each new retrieval task is resource…
DPTDR: Deep Prompt Tuning for Dense Passage Retrieval
Zhengyang Tang, Benyou Wang, Ting Yao
Deep prompt tuning (DPT) has gained great success in most natural language processing~(NLP) tasks. However, it is not well-investigated in dense retrieval where fine-tuning~(FT) st…