23 citations · 59 across the 11 of their papers we have counts for
11 papers
Overview of the NLPCC 2024 Shared Task on Chinese Metaphor Generation
Xingwei Qu, Ge Zhang, Siwei Wu +2
This paper presents the results of the shared task on Chinese metaphor generation, hosted at the 13th CCF Conference on Natural Language Processing and Chinese Computing (NLPCC 202…
MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models
Siwei Wu, Kang Zhu, Yu Bai +10
Given the remarkable success that large visual language models (LVLMs) have achieved in image perception tasks, the endeavor to make LVLMs perceive the world like humans is drawing…
McEval: Massively Multilingual Code Evaluation
Linzheng Chai, Shukai Liu, Jian Yang +15
Code large language models (LLMs) have shown remarkable advances in code understanding, completion, and generation tasks. Programming benchmarks, comprised of a selection of code c…
D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models
Haoran Que, Jiaheng Liu, Ge Zhang +13
Continual Pre-Training (CPT) on Large Language Models (LLMs) has been widely used to expand the model's fundamental understanding of specific downstream domains (e.g., math and cod…
E^2-LLM: Efficient and Extreme Length Extension of Large Language Models
Jiaheng Liu, Zhiqi Bai, Yuanxing Zhang +11
Typically, training LLMs with long context sizes is computationally expensive, requiring extensive training hours and GPU resources. Existing long-context extension methods usually…
MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning
Xiang Yue, Xingwei Qu, Ge Zhang +5
We introduce MAmmoTH, a series of open-source large language models (LLMs) specifically tailored for general math problem-solving. The MAmmoTH models are trained on MathInstruct, o…