--- license: apache-2.0 dataset_info: - config_name: graph_connectivity features: - name: image dtype: image - name: query_nodes_color dtype: string - name: adjacency_matrix dtype: string - name: query_node_1 dtype: int64 - name: query_node_2 dtype: int64 - name: label dtype: bool - name: id dtype: string splits: - name: validation num_bytes: 62682553 num_examples: 128 download_size: 19391513 dataset_size: 62682553 - config_name: graph_isomorphism features: - name: image dtype: image - name: adjacency_matrix_G dtype: string - name: adjacency_matrix_H dtype: string - name: label dtype: bool - name: id dtype: string splits: - name: validation num_bytes: 25082487 num_examples: 128 download_size: 8931620 dataset_size: 25082487 - config_name: graph_maxflow features: - name: image dtype: image - name: source_node dtype: int64 - name: source_node_color dtype: string - name: sink_node dtype: int64 - name: sink_node_color dtype: string - name: adjacency_matrix dtype: string - name: label dtype: int64 - name: id dtype: string splits: - name: validation num_bytes: 44530168 num_examples: 128 download_size: 16112025 dataset_size: 44530168 - config_name: math_breakpoint features: - name: image dtype: image - name: xlim dtype: float64 - name: latex dtype: string - name: code dtype: string - name: label dtype: int64 - name: id dtype: string splits: - name: validation num_bytes: 14120119 num_examples: 256 download_size: 12531433 dataset_size: 14120119 - config_name: math_convexity features: - name: image dtype: image - name: xlim dtype: string - name: latex dtype: string - name: code dtype: string - name: label dtype: string - name: id dtype: string splits: - name: validation num_bytes: 11176740 num_examples: 256 download_size: 9253901 dataset_size: 11176740 - config_name: math_parity features: - name: image dtype: image - name: xlim dtype: float64 - name: latex dtype: string - name: code dtype: string - name: label dtype: string - name: id dtype: string splits: - name: validation num_bytes: 16746427 num_examples: 378 download_size: 14069277 dataset_size: 16746427 configs: - config_name: graph_connectivity data_files: - split: validation path: graph_connectivity/validation-* - config_name: graph_isomorphism data_files: - split: validation path: graph_isomorphism/validation-* - config_name: graph_maxflow data_files: - split: validation path: graph_maxflow/validation-* - config_name: math_breakpoint data_files: - split: validation path: math_breakpoint/validation-* - config_name: math_convexity data_files: - split: validation path: math_convexity/validation-* - config_name: math_parity data_files: - split: validation path: math_parity/validation-* language: - en --- # Dataset Card for IsoBench 📚 [paper](https://arxiv.org/abs/2404.01266) 🌐 [website](https://isobench.github.io) Introducing IsoBench, a benchmark dataset containing problems from four major areas: math, science, algorithms, and games. Each example is presented with multiple isomorphic representations of inputs, such as visual, textual, and mathematical presentations. Details of IsoBench can be found in our [paper](https://arxiv.org/abs/2404.01266) or [website](https://isobench.github.io)! ## Table of Contents - [Dataset Details](#dataset-details) - [Mathematics](#mathematics) - [Algorithms](#algorithms) - [Games](#games) - [Science](#science) - [Data Fields](#deta-fields) - [Mathematics](#mathematics) - [Convexity](#convexity) - [Breakpoint](#breakpoint) - [Parity](#parity) - [Algorithms](#algorithms) - [Connectivity](#connectivity) - [Maxflow](#maxflow) - [Isomorphism](#isomorphism) - [Games](#games) - [Winner Identification](#winner-identification) - [Chess Puzzle](#chess-puzzle) - [Science](#science) - [Chemistry](#chemistry) - [Physics](#physics) - [Citation](#citation) - [Contact](#contact) ## Uses There are 4 major domains: math, algorithm, game, and science. Each domain has several subtasks. We will show how to load the data for each subtask. ### Direct Use IsoBench is designed with two objectives, which are: - Analyzing the behavior difference between language-only and multimodal foundation models, by prompting them with distinct (*e.g.* mathematical expression and plot of a function) representations of the same input. - Contributing a language-only/multimodal benchmark in the science domain. #### Mathematics There are three mathematics tasks. Each task is structured as a classification problem and each class contains 128 samples. - **Parity** implements a ternary classification problem. A model has to classify an input function into an even function, odd function, or neither. - **Convexity** implements a binary classification problem for a model to classify an input function as convex or concave. **Note**: some functions are only convex (resp. concave) within a certain domain (*e.g.* `x > 0`), which is reported in the `xlim` field of each sample. We recommend providing this information as part of the prompt! - **Breakpoint** counts the number of breakpoints (*i.e.* intersections of a piecewise linear function). Each function contains either 2 or 3 breakpoints, which renders this task a binary classification problem. ```python from datasets import load_dataset dataset_connectivity = load_dataset('isobench/IsoBench', 'math_parity', split='validation') dataset_maxflow = load_dataset('isobench/IsoBench', 'math_convexity', split='validation') dataset_isomorphism = load_dataset('isobench/IsoBench', 'math_breakpoint', split='validation') ``` ### Algorithms There are three algorithmic tasks, with ascending complexity: graph connectivity, graph maximum flow, and graph isomorphism. You can download the data by ```python from datasets import load_dataset dataset_connectivity = load_dataset('isobench/IsoBench', 'graph_connectivity', split='validation') dataset_maxflow = load_dataset('isobench/IsoBench', 'graph_maxflow', split='validation') dataset_isomorphism = load_dataset('isobench/IsoBench', 'graph_isomorphism', split='validation') ``` Each task has 128 dev samples under the validation split. ### Games [More Information Needed] ### Science [More Information Needed] ## Data Fields ### Mathematics [More Information Needed] ### Algorithms #### Connectivity - `image`: a PIL Image feature - `query_nodes_color`: a `string` feature - `adjacency_matrix`: a `string` feature, a string of an 2d array representing the adjacency matrix of a graph - `query_node_1`: a `unit32` feature - `query_node_2`: a `unit32` feature - `label`: a `bool` feature, with possible values including `True` (query nodes connected) and `False` (query nodes not connected) - `id`: a `string` feature #### Maxflow #### Isomorphism ### Games [More Information Needed] ### Science [More Information Needed] ## Citation **BibTeX:** ```BibTeX @misc{fu2024isobench, title={{I}so{B}ench: Benchmarking Multimodal Foundation Models on Isomorphic Representations}, author={Deqing Fu$^*$ and Ghazal Khalighinejad$^*$ and Ollie Liu$^*$ and Bhuwan Dhingra and Dani Yogatama and Robin Jia and Willie Neiswanger}, year={2024}, eprint={2404.01266}, archivePrefix={arXiv}, primaryClass={cs.AI} } ``` **Chicago:** Fu, Deqing, Ghazal Khalighinejad, Ollie Liu, Bhuwan Dhingra, Dani Yogatama, Robin Jia, and Willie Neiswanger. "IsoBench: Benchmarking Multimodal Foundation Models on Isomorphic Representations." arXiv preprint arXiv:2404.01266 (2024). ## Contact deqingfu@usc.edu, me@ollieliu.com, ghazal.khalighinejad@duke.edu