Search Results for author: Yiming Liang

Found 15 papers, 8 papers with code

Aligning Instruction Tuning with Pre-training

no code implementations16 Jan 2025 Yiming Liang, Tianyu Zheng, Xinrun Du, Ge Zhang, Jiaheng Liu, Xingwei Qu, Wenqiang Zu, Xingrun Xing, Chujie Zheng, Lei Ma, Wenhu Chen, Guoyin Wang, Zhaoxiang Zhang, Wenhao Huang, Xiang Yue, Jiajun Zhang

Instruction tuning enhances large language models (LLMs) to follow human instructions across diverse tasks, relying on high-quality datasets to guide behavior.

Diversity

A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer

no code implementations15 Dec 2024 Hanxiao Lu, Hongyu Cai, Yiming Liang, Antonio Bianchi, Z. Berkay Celik

Language model approaches have recently been integrated into binary analysis tasks, such as function similarity detection and function signature recovery.

Feature Engineering Language Modeling +3

Can MLLMs Understand the Deep Implication Behind Chinese Images?

1 code implementation17 Oct 2024 Chenhao Zhang, Xi Feng, Yuelin Bai, Xinrun Du, Jinchang Hou, Kaixin Deng, Guangzeng Han, Qinrui Li, Bingli Wang, Jiaheng Liu, Xingwei Qu, Yifei Zhang, Qixuan Zhao, Yiming Liang, Ziqiang Liu, Feiteng Fang, Min Yang, Wenhao Huang, Chenghua Lin, Ge Zhang, Shiwen Ni

To fill the gap, we introduce the **C**hinese **I**mage **I**mplication understanding **Bench**mark, **CII-Bench**, which aims to assess the higher-order perception and understanding capabilities of MLLMs for Chinese images.

I-SHEEP: Self-Alignment of LLM from Scratch through an Iterative Self-Enhancement Paradigm

1 code implementation15 Aug 2024 Yiming Liang, Ge Zhang, Xingwei Qu, Tianyu Zheng, Jiawei Guo, Xinrun Du, Zhenzhu Yang, Jiaheng Liu, Chenghua Lin, Lei Ma, Wenhao Huang, Jiajun Zhang

Large Language Models (LLMs) have achieved significant advancements, however, the common learning paradigm treats LLMs as passive information repositories, neglecting their potential for active learning and alignment.

Active Learning Code Generation

TEGEE: Task dEfinition Guided Expert Ensembling for Generalizable and Few-shot Learning

no code implementations7 Mar 2024 Xingwei Qu, Yiming Liang, Yucheng Wang, Tianyu Zheng, Tommy Yue, Xingyuan Bu, Lei Ma, Stephen W. Huang, Jiajun Zhang, Yinan Shi, Chenghua Lin, Jie Fu, Ge Zhang

Our framework employs a dual 3B model approach, with each model assigned a distinct role: one focuses on task definition extraction, while the other handles learning from demonstrations.

Continual Learning Definition Extraction +3

m2mKD: Module-to-Module Knowledge Distillation for Modular Transformers

1 code implementation26 Feb 2024 Ka Man Lo, Yiming Liang, Wenyu Du, Yuantao Fan, Zili Wang, Wenhao Huang, Lei Ma, Jie Fu

Additionally, the V-MoE-Base model trained with m2mKD achieves 3. 5% higher accuracy than end-to-end training on ImageNet-1k.

Knowledge Distillation

CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models

no code implementations20 Feb 2024 Yizhi Li, Ge Zhang, Xingwei Qu, Jiali Li, Zhaoqun Li, Zekun Wang, Hao Li, Ruibin Yuan, Yinghao Ma, Kai Zhang, Wangchunshu Zhou, Yiming Liang, Lei Zhang, Lei Ma, Jiajun Zhang, Zuowen Li, Stephen W. Huang, Chenghua Lin, Jie Fu

The advancement of large language models (LLMs) has enhanced the ability to generalize across a wide range of unseen natural language processing (NLP) tasks through instruction-following.

Instruction Following

SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval

1 code implementation24 Jan 2024 Siwei Wu, Yizhi Li, Kang Zhu, Ge Zhang, Yiming Liang, Kaijing Ma, Chenghao Xiao, Haoran Zhang, Bohao Yang, Wenhu Chen, Wenhao Huang, Noura Al Moubayed, Jie Fu, Chenghua Lin

We further annotate the image-text pairs with two-level subset-subcategory hierarchy annotations to facilitate a more comprehensive evaluation of the baselines.

Benchmarking Image Captioning +3

CMMMU: A Chinese Massive Multi-discipline Multimodal Understanding Benchmark

1 code implementation22 Jan 2024 Ge Zhang, Xinrun Du, Bei Chen, Yiming Liang, Tongxu Luo, Tianyu Zheng, Kang Zhu, Yuyang Cheng, Chunpu Xu, Shuyue Guo, Haoran Zhang, Xingwei Qu, Junjie Wang, Ruibin Yuan, Yizhi Li, Zekun Wang, Yudong Liu, Yu-Hsuan Tsai, Fengji Zhang, Chenghua Lin, Wenhao Huang, Jie Fu

We introduce CMMMU, a new Chinese Massive Multi-discipline Multimodal Understanding benchmark designed to evaluate LMMs on tasks demanding college-level subject knowledge and deliberate reasoning in a Chinese context.

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