1 citations · 1 across the 24 of their papers we have counts for
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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…
EvolProver: Advancing Automated Theorem Proving by Evolving Formalized Problems via Symmetry and Difficulty
Yuchen Tian, Ruiyuan Huang, Xuanwu Wang +6
Large Language Models (LLMs) for formal theorem proving have shown significant promise, yet they often lack generalizability and are fragile to even minor transformations of proble…
AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries?
Yuchen Tian, Kaixin Li, Hao Chen +5
Large Language Models (LLMs) have recently demonstrated strong capabilities in translating natural language into database queries, especially when dealing with complex graph-struct…
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…
ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use
Kaixin Li, Ziyang Meng, Hongzhan Lin +5
Recent advancements in Multi-modal Large Language Models (MLLMs) have led to significant progress in developing GUI agents for general tasks such as web browsing and mobile phone u…
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…