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  data_files:
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  - split: val
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  path: spatial_map/spatial_map_text_only_val.parquet
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  data_files:
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  - split: val
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  path: spatial_map/spatial_map_text_only_val.parquet
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+ ---
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+
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+ A key question for understanding multimodal vs. language capabilities of models is what is
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+ the relative strength of the spatial reasoning and understanding in each modality, as spatial understanding is
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+ expected to be a strength for multimodality? To test this we created a procedurally generatable, synthetic dataset
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+ to testing spatial reasoning, navigation, and counting. These datasets are challenging and by
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+ being procedurally generated new versions can easily be created to combat the effects of models being trained
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+ on this data and the results being due to memorization. For each task, each question has an image and a text
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+ representation that is sufficient for answering each question.
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+
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+
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+ This dataset has three tasks that test: Spatial Understanding (Spatial-Map), Nav-
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+ igation (Maze), and Counting (Spatial-Grid). Each task has three conditions, with respect to the input
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+ modality, 1) text-only, input and a question, 2) vision-only, which is the standard task of visual-question an-
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+ swering that consists of a vision-only input and a question, and 3) vision-text includes both text and image
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+ representations with the question. Each condition includes 1500
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+ images and text pairs for a total of 4500.
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+
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+ __Spatial Map__
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+
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+ The dataset consists of spatial relationships for random layouts of symbolic objects with text names on white background.
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+ Each object is associated with a unique location name, such as Unicorn Umbrellas and Gale Gifts. To study the impact of modality,
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+ the textual representation of each input consists of pairwise relations such as Brews Brothers Pub
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+ is to the Southeast of Whale’s Watches. The questions include asking about the spatial
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+ relationships between two locations and the number of objects that meet specific spatial criteria.
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+
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+ The dataset includes 3 conditions: text only, image only, and text+image. Each condition includes 1500 images and text pairs for a total of 4500.
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+
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+ There are 3 question types:
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+ 1) In which direction is one object to another (answer is a direction)
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+ 2) Which object is to the direction of another (answer is an object name)
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+ 3) How many objects are in a direction of another (answer is a number)
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+
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+ Each question is multiple choice.
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+
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+ __Maze__
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+
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+ The dataset consists of small mazes with questions asked about the maze. Each sample can be
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+ represented as colored blocks where different colors signify distinct elements: a green block marks
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+ the starting point (S), a red block indicates the exit (E), black blocks represent impassable walls,
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+ white blocks denote navigable paths, and blue blocks trace the path from S to E. The objective is to
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+ navigate from S to E following the blue path, with movement permitted in the four cardinal directions
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+ (up, down, left, right). Alternatively, each input can be depicted in textual format using ASCII code.
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+ The questions asked include counting the number of turns from S to E and determining the spatial relationship
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+ between S and E.
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+
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+ The dataset includes 3 conditions: text only, image only, and text+image. Each condition includes 1500 images and text pairs for a total of 4500.
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+
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+ There are 3 question types:
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+ 1) How many right turns on the path from start to end (answer is a number)
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+ 2) How many total turns on the path from start to end (answer is a number)
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+ 3) Where is the exit releative to the start (answer is direction or yes/no)
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+
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+ Each question is multiple choice.
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+
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+ __Spatial Grid__
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+
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+ Each input consists of a grid of cells, each containing an image (e.g.,a rabbit). Alternatively, this grid
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+ can also be represented in a purely textual format; for instance, the first row might be described as:
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+ elephant | cat | giraffe | elephant | cat. The evaluations focus on tasks such as counting specific objects (e.g., rabbits) and
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+ identifying the object located at a specific coordinate in the grid (e.g., first row, second column).
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+
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+ The dataset includes 3 conditions: text only, image only, and text+image. Each condition includes 1500 images and text pairs for a total of 4500 questions.
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+
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+ There are 3 question types:
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+ 1) How many blocks contain a specific animal (answer is a number)
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+ 2) What animal is in one specific block, adressed by top-left, top, right, etc. (answer is an animal name)
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+ 3) What animal is in one specific block, addressed by row, column (answer is an animal name)
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+
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+ Each question is multiple choice.
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+
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+ ---
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+ More details here: https://arxiv.org/pdf/2406.14852