5 citations · 13 across the 11 of their papers we have counts for
11 papers
OpenOmni: Advancing Open-Source Omnimodal Large Language Models with Progressive Multimodal Alignment and Real-Time Self-Aware Emotional Speech Synthesis
Run Luo, Ting-En Lin, Haonan Zhang +10
Recent advancements in omnimodal learning have significantly improved understanding and generation across images, text, and speech, yet these developments remain predominantly conf…
IP-MOT: Instance Prompt Learning for Cross-Domain Multi-Object Tracking
Run Luo, Zikai Song, Longze Chen +3
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. Most e…
Ruler: A Model-Agnostic Method to Control Generated Length for Large Language Models
Jiaming Li, Lei Zhang, Yunshui Li +5
The instruction-following ability of large language models enables humans to interact with AI agents in a natural way. However, when required to generate responses of a specific le…
MMEvol: Empowering Multimodal Large Language Models with Evol-Instruct
Run Luo, Haonan Zhang, Longze Chen +13
The development of Multimodal Large Language Models (MLLMs) has seen significant advancements with increasing demands in various fields (e.g., multimodal agents, embodied intellige…
Hierarchical Context Pruning: Optimizing Real-World Code Completion with Repository-Level Pretrained Code LLMs
Lei Zhang, Yunshui Li, Jiaming Li +7
Some recently developed code large language models (Code LLMs) have been pre-trained on repository-level code data (Repo-Code LLMs), enabling these models to recognize repository s…
Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA
Minzheng Wang, Longze Chen, Cheng Fu +11
Long-context modeling capabilities have garnered widespread attention, leading to the emergence of Large Language Models (LLMs) with ultra-context windows. Meanwhile, benchmarks fo…
Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language Models
Longze Chen, Ziqiang Liu, Wanwei He +3
Long-context modeling capabilities are important for large language models (LLMs) in various applications. However, directly training LLMs with long context windows is insufficient…
DEEM: Diffusion Models Serve as the Eyes of Large Language Models for Image Perception
Run Luo, Yunshui Li, Longze Chen +9
The development of large language models (LLMs) has significantly advanced the emergence of large multimodal models (LMMs). While LMMs have achieved tremendous success by promoting…
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning
Yuelin Bai, Xinrun Du, Yiming Liang +19
Remarkable progress on English instruction tuning has facilitated the efficacy and reliability of large language models (LLMs). However, there remains a noticeable gap in instructi…
One-Shot Learning as Instruction Data Prospector for Large Language Models
Yunshui Li, Binyuan Hui, Xiaobo Xia +9
Contemporary practices in instruction tuning often hinge on enlarging data scaling without a clear strategy for ensuring data quality, inadvertently introducing noise that may comp…
Marathon: A Race Through the Realm of Long Context with Large Language Models
Lei Zhang, Yunshui Li, Ziqiang Liu +5
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' c…