4 citations · 4 across the 9 of their papers we have counts for
5 papers · 1 filter
LaoBench: A Large-Scale Multidimensional Lao Benchmark for Large Language Models
Jian Gao, Richeng Xuan, Zhaolu Kang +9
The rapid advancement of large language models (LLMs) has not been matched by their evaluation in low-resource languages, especially Southeast Asian languages like Lao. To fill thi…
Do Vision-Language Models Measure Up? Benchmarking Visual Measurement Reading with MeasureBench
Fenfen Lin, Yesheng Liu, Haiyu Xu +7
Reading measurement instruments is effortless for humans and requires relatively little domain expertise, yet it remains surprisingly challenging for current vision-language models…
FlagEval Findings Report: A Preliminary Evaluation of Large Reasoning Models on Automatically Verifiable Textual and Visual Questions
Bowen Qin, Chen Yue, Fang Yin +26
We conduct a moderate-scale contamination-free (to some extent) evaluation of current large reasoning models (LRMs) with some preliminary findings. We also release ROME, our evalua…
FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model Evaluation
Zheqi He, Yesheng Liu, Jing-shu Zheng +5
We present FlagEvalMM, an open-source evaluation framework designed to comprehensively assess multimodal models across a diverse range of vision-language understanding and generati…
Video-SafetyBench: A Benchmark for Safety Evaluation of Video LVLMs
Xuannan Liu, Zekun Li, Zheqi He +6
The increasing deployment of Large Vision-Language Models (LVLMs) raises safety concerns under potential malicious inputs. However, existing multimodal safety evaluations primarily…