VALUE-Bench: A Comprehensive Benchmark for Evaluating Large Vision-Language Models on Multimodal Ethical Understanding
Authors: Xuan Feng, Mingfeng Feng, Ning Zhang, Tianlong Gu, Liang Chang, Xiaoli Liu


Reviewer paper summaries: The paper develops a benchmark for evaluating large vision-language models in terms of four aspects: ethical understanding, robustness, reliability, and resistance to misuse.
Summary of strengths: The paper is centered around an important and worthy topic. The problem and motivation are clear. The authors used 6 different publically available datasets in evaluation. The details of the work done are presented.
Summary of weaknesses: The main argument of the authors for proposing a new benchmark is an ethical understanding that previous benchmarks have not addressed. However, I am not confident the proposed benchmark addresses that. There is no convincing argument in Section 3.1 that the Triple-check strategy addresses ethical understanding. 

Additionally, in general, there are no convincing arguments and evidence (references) to back the choice of the strategies used in the benchmark. This opens the potential for bias in the arguments for the novelty or superiority of the benchmark compared to the existing benchmarks.
Comments, suggestions and typos: This paper could benefit from more convincing arguments backed by referencing, specifically on the strategies used in developing the benchmark. 

Some of the sentences are also unnecessarily long or verbose.
Overall assessment scores: 2.5