Figure 4: Our designed prompts without the Chain-of-Thought idea. Task 3(b) is for inquiries. Figure 5: Our designed prompts with the Chain-of-Thought idea. Task 3(b) is for inquiries. Thought prompts for Task 1 and Task 2 are presented in Figure 5. Scoring Objective For the LLMs' output response to each input cunning text, please refer to the Scoring Rules The scoring values are defined as {1, 2, 3, 4, 5}.

Inspired by the above motivation, we collect real cunning texts as our raw data from a famous Chinese online forum, the "Ruozhiba" (retard forum)

VLC is a benchmark for Bidirectional Vision-Language Composition-ality evaluation. Each instance consists of two images and two captions.

A.1 Motivation For what purpose was the dataset created?

The attribute extractions of these notes were then verified for by authors of this paper.

Large Multimodal Models (LMMs) have shown remarkable capabilities across a variety of tasks (e.g., image captioning, visual question answering). While broad, their knowledge remains generic (e.g., recognizing a dog), and they are

Terms are grouped to five main types including location, activities, weather, misc, and camera types. Locations and activities include two levels of hierarchies. The structure of search terms changes based on the selected sub-group. Using an exhaustive list of locations is not feasible given the search space. 'scenic' as actions are less relevant when the locations are broad.

This dataset encompasses human preferences in text-to-video generation tasks along two primary dimensions: helpfulness and harmlessness.

Literal Interpretation: One of the limitations is the model's tendency to interpret questions