Search Results for author: Yunshui Li

Found 14 papers, 11 papers with code

Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey

1 code implementation16 Dec 2024 Liang Chen, Zekun Wang, Shuhuai Ren, Lei LI, Haozhe Zhao, Yunshui Li, Zefan Cai, Hongcheng Guo, Lei Zhang, Yizhe Xiong, Yichi Zhang, Ruoyu Wu, Qingxiu Dong, Ge Zhang, Jian Yang, Lingwei Meng, Shujie Hu, Yulong Chen, Junyang Lin, Shuai Bai, Andreas Vlachos, Xu Tan, Minjia Zhang, Wen Xiao, Aaron Yee, Tianyu Liu, Baobao Chang

As Large Language Models (LLMs) have advanced to unify understanding and generation tasks within the textual modality, recent research has shown that tasks from different modalities can also be effectively encapsulated within the NTP framework, transforming the multimodal information into tokens and predict the next one given the context.

Language Modeling Language Modelling +2

IP-MOT: Instance Prompt Learning for Cross-Domain Multi-Object Tracking

no code implementations30 Oct 2024 Run Luo, Zikai Song, Longze Chen, Yunshui Li, Min Yang, Wei Yang

Multi-Object Tracking (MOT) aims to associate multiple objects across video frames and is a challenging vision task due to inherent complexities in the tracking environment.

Knowledge Distillation Language Modelling +2

Ruler: A Model-Agnostic Method to Control Generated Length for Large Language Models

1 code implementation27 Sep 2024 Jiaming Li, Lei Zhang, Yunshui Li, Ziqiang Liu, Yuelin Bai, Run Luo, Longze Chen, Min Yang

Specifically, Ruler equips LLMs with the ability to generate responses of a specified length based on length constraints within the instructions.

Instruction Following

MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct

no code implementations9 Sep 2024 Run Luo, Haonan Zhang, Longze Chen, Ting-En Lin, Xiong Liu, Yuchuan Wu, Min Yang, Minzheng Wang, Pengpeng Zeng, Lianli Gao, Heng Tao Shen, Yunshui Li, Xiaobo Xia, Fei Huang, Jingkuan Song, Yongbin Li

This framework iteratively improve data quality through a refined combination of fine-grained perception, cognitive reasoning, and interaction evolution, generating a more complex and diverse image-text instruction dataset that empowers MLLMs with enhanced capabilities.

Diversity Visual Reasoning

Hierarchical Context Pruning: Optimizing Real-World Code Completion with Repository-Level Pretrained Code LLMs

1 code implementation26 Jun 2024 Lei Zhang, Yunshui Li, Jiaming Li, Xiaobo Xia, Jiaxi Yang, Run Luo, Minzheng Wang, Longze Chen, Junhao Liu, Min Yang

We applied the HCP strategy in experiments with six Repo-Code LLMs, and the results demonstrate that our proposed method can significantly enhance completion accuracy while substantially reducing the length of input.

Code Completion

Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA

1 code implementation25 Jun 2024 Minzheng Wang, Longze Chen, Cheng Fu, Shengyi Liao, Xinghua Zhang, Bingli Wu, Haiyang Yu, Nan Xu, Lei Zhang, Run Luo, Yunshui Li, Min Yang, Fei Huang, Yongbin Li

Long-context modeling capabilities have garnered widespread attention, leading to the emergence of Large Language Models (LLMs) with ultra-context windows.

Benchmarking Long-Context Understanding +2

Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language Models

1 code implementation28 May 2024 Longze Chen, Ziqiang Liu, Wanwei He, Yunshui Li, Run Luo, Min Yang

In this study, we propose a data mining framework \textbf{ProLong} that can assign each training sample with a long dependency score, which can be used to rank and filter samples that are more advantageous for enhancing long-context modeling abilities in LLM training.

Computational Efficiency Specificity

One-Shot Learning as Instruction Data Prospector for Large Language Models

1 code implementation16 Dec 2023 Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Ling-Hao Chen, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li

Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality, inadvertently introducing noise that may compromise model performance.

One-Shot Learning

Marathon: A Race Through the Realm of Long Context with Large Language Models

1 code implementation15 Dec 2023 Lei Zhang, Yunshui Li, Ziqiang Liu, Jiaxi Yang, Junhao Liu, Longze Chen, Run Luo, Min Yang

With the advancement of large language models (LLMs) and the expansion of their context windows, existing long-context benchmarks fall short in effectively evaluating the models' comprehension and reasoning abilities in extended texts.

Long-Context Understanding Multiple-choice

VDialogUE: A Unified Evaluation Benchmark for Visually-grounded Dialogue

no code implementations14 Sep 2023 Yunshui Li, Binyuan Hui, Zhaochao Yin, Wanwei He, Run Luo, Yuxing Long, Min Yang, Fei Huang, Yongbin Li

Visually-grounded dialog systems, which integrate multiple modes of communication such as text and visual inputs, have become an increasingly popular area of investigation.

PaCE: Unified Multi-modal Dialogue Pre-training with Progressive and Compositional Experts

1 code implementation24 May 2023 Yunshui Li, Binyuan Hui, Zhichao Yin, Min Yang, Fei Huang, Yongbin Li

It utilizes a combination of several fundamental experts to accommodate multiple dialogue-related tasks and can be pre-trained using limited dialogue and extensive non-dialogue multi-modal data.

Dialogue State Tracking Image Retrieval +4

Self-Distillation with Meta Learning for Knowledge Graph Completion

1 code implementation Findings of the Association for Computational Linguistics: EMNLP 2022 2022 Yunshui Li, Junhao Liu, Chengming Li, Min Yang

In this paper, we propose a selfdistillation framework with meta learning(MetaSD) for knowledge graph completion with dynamic pruning, which aims to learn compressed graph embeddings and tackle the longtail samples.

Knowledge Graph Completion Meta-Learning +1

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