activity
20242026
most citedGraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability

1 citations · 2 across the 8 of their papers we have counts for

collaborators

8 papers

cs.RO2026

OneVLA: A Unified Framework for Embodied Tasks

Lingfeng Zhang, Xiaoshuai Hao, Yingbo Tang +10

Navigation and manipulation are fundamental capabilities of embodied intelligence, enabling robots to interpret natural language commands and interact physically with their surroun…

cs.CV2026

MetaphorStar: Image Metaphor Understanding and Reasoning with End-to-End Visual Reinforcement Learning

Chenhao Zhang, Yazhe Niu, Hongsheng Li

Metaphorical comprehension in images remains a critical challenge for Nowadays AI systems. While Multimodal Large Language Models (MLLMs) excel at basic Visual Question Answering (…

cs.CV2025

Let Androids Dream of Electric Sheep: A Human-Inspired Image Implication Understanding and Reasoning Framework

Chenhao Zhang, Yazhe Niu

Metaphorical comprehension in images remains a critical challenge for AI systems, as existing models struggle to grasp the nuanced cultural, emotional, and contextual implications…

cs.CL2024

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…

cs.CL2024★ 1 cited

CPsyCoun: A Report-based Multi-turn Dialogue Reconstruction and Evaluation Framework for Chinese Psychological Counseling

Chenhao Zhang, Renhao Li, Minghuan Tan +7

Using large language models (LLMs) to assist psychological counseling is a significant but challenging task at present. Attempts have been made on improving empathetic conversation…

cs.CL2024

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…

cs.CL2024

CPsyExam: A Chinese Benchmark for Evaluating Psychology using Examinations

Jiahao Zhao, Jingwei Zhu, Minghuan Tan +8

In this paper, we introduce a novel psychological benchmark, CPsyExam, constructed from questions sourced from Chinese language examinations. CPsyExam is designed to prioritize psy…

cs.AI2024★ 1 cited

GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability

Zihan Luo, Xiran Song, Hong Huang +5

Improving the general capabilities of large language models (LLMs) is an active research topic. As a common data structure in many real-world domains, understanding graph data is a…