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Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checking
Hongzhan Lin, Zixin Chen, Zhiqi Shen +5
Large Language Models (LLMs) are increasingly deployed in real-world fact-checking systems, yet existing evaluations focus predominantly on claim verification and overlook the broa…
DiffCoT: Diffusion-styled Chain-of-Thought Reasoning in LLMs
Shidong Cao, Hongzhan Lin, Yuxuan Gu +2
Chain-of-Thought (CoT) reasoning improves multi-step mathematical problem solving in large language models but remains vulnerable to exposure bias and error accumulation, as early…
MM-CRITIC: A Holistic Evaluation of Large Multimodal Models as Multimodal Critique
Gailun Zeng, Ziyang Luo, Hongzhan Lin +5
The ability of critique is vital for models to self-improve and serve as reliable AI assistants. While extensively studied in language-only settings, multimodal critique of Large M…
MemeArena: Automating Context-Aware Unbiased Evaluation of Harmfulness Understanding for Multimodal Large Language Models
Zixin Chen, Hongzhan Lin, Kaixin Li +3
The proliferation of memes on social media necessitates the capabilities of multimodal Large Language Models (mLLMs) to effectively understand multimodal harmfulness. Existing eval…
AdamMeme: Adaptively Probe the Reasoning Capacity of Multimodal Large Language Models on Harmfulness
Zixin Chen, Hongzhan Lin, Kaixin Li +5
The proliferation of multimodal memes in the social media era demands that multimodal Large Language Models (mLLMs) effectively understand meme harmfulness. Existing benchmarks for…
FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models
Hongzhan Lin, Yang Deng, Yuxuan Gu +4
Large Language Models (LLMs) have significantly advanced the fact-checking studies. However, existing automated fact-checking evaluation methods rely on static datasets and classif…
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…