5 citations · 9 across the 13 of their papers we have counts for
7 papers · 1 filter
Can MLLMs Understand the Deep Implication Behind Chinese Images?
Chenhao Zhang, Xi Feng, Yuelin Bai +18
As the capabilities of Multimodal Large Language Models (MLLMs) continue to improve, the need for higher-order capability evaluation of MLLMs is increasing. However, there is a lac…
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…
AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents
Guhong Chen, Liyang Fan, Zihan Gong +8
Current research in LLM-based simulation systems lacks comprehensive solutions for modeling real-world court proceedings, while existing legal language models struggle with dynamic…
II-Bench: An Image Implication Understanding Benchmark for Multimodal Large Language Models
Ziqiang Liu, Feiteng Fang, Xi Feng +23
The rapid advancements in the development of multimodal large language models (MLLMs) have consistently led to new breakthroughs on various benchmarks. In response, numerous challe…
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…