1 citations · 1 across the 24 of their papers we have counts for
7 papers · 2 filters
ScratchEval: Are GPT-4o Smarter than My Child? Evaluating Large Multimodal Models with Visual Programming Challenges
Rao Fu, Ziyang Luo, Hongzhan Lin +2
Recent advancements in large multimodal models (LMMs) have showcased impressive code generation capabilities, primarily evaluated through image-to-code benchmarks. However, these b…
Towards Low-Resource Harmful Meme Detection with LMM Agents
Jianzhao Huang, Hongzhan Lin, Ziyan Liu +3
The proliferation of Internet memes in the age of social media necessitates effective identification of harmful ones. Due to the dynamic nature of memes, existing data-driven model…
SHARP: Unlocking Interactive Hallucination via Stance Transfer in Role-Playing LLMs
Chuyi Kong, Ziyang Luo, Hongzhan Lin +4
The advanced role-playing capabilities of Large Language Models (LLMs) have enabled rich interactive scenarios, yet existing research in social interactions neglects hallucination…
AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation
Ziyang Luo, Xin Li, Hongzhan Lin +2
The impressive performance of proprietary LLMs like GPT4 in code generation has led to a trend to replicate these capabilities in open-source models through knowledge distillation…
MFC-Bench: Benchmarking Multimodal Fact-Checking with Large Vision-Language Models
Shengkang Wang, Hongzhan Lin, Ziyang Luo +3
Large vision-language models (LVLMs) have significantly improved multimodal reasoning tasks, such as visual question answering and image captioning. These models embed multimodal f…
CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models
Hongzhan Lin, Zixin Chen, Ziyang Luo +3
Social media abounds with multimodal sarcasm, and identifying sarcasm targets is particularly challenging due to the implicit incongruity not directly evident in the text and image…
MMCode: Benchmarking Multimodal Large Language Models for Code Generation with Visually Rich Programming Problems
Kaixin Li, Yuchen Tian, Qisheng Hu +3
Programming often involves converting detailed and complex specifications into code, a process during which developers typically utilize visual aids to more effectively convey conc…