| --- |
| language: |
| - en |
| license: cc-by-sa-4.0 |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - text-classification |
| - zero-shot-classification |
| - image-classification |
| pretty_name: IsoBench |
| dataset_info: |
| - config_name: chemistry |
| features: |
| - name: image |
| dtype: image |
| - name: question |
| dtype: string |
| - name: choices |
| dtype: string |
| - name: label |
| dtype: int64 |
| - name: description |
| dtype: string |
| - name: id |
| dtype: string |
| splits: |
| - name: validation |
| num_bytes: 2611154.0 |
| num_examples: 75 |
| download_size: 2517594 |
| dataset_size: 2611154.0 |
| - 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: domain |
| 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: 12531449 |
| dataset_size: 14120119 |
| - config_name: math_convexity |
| features: |
| - name: image |
| dtype: image |
| - name: domain |
| 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: 9253917 |
| dataset_size: 11176740 |
| - config_name: math_parity |
| features: |
| - name: image |
| dtype: image |
| - name: domain |
| dtype: float64 |
| - name: latex |
| dtype: string |
| - name: code |
| dtype: string |
| - name: label |
| dtype: string |
| - name: id |
| dtype: string |
| splits: |
| - name: validation |
| num_bytes: 17012598 |
| num_examples: 384 |
| download_size: 14230745 |
| dataset_size: 17012598 |
| - config_name: physics |
| features: |
| - name: image |
| dtype: image |
| - name: question |
| dtype: string |
| - name: choices |
| dtype: string |
| - name: label |
| dtype: int64 |
| - name: description |
| dtype: string |
| - name: id |
| dtype: string |
| splits: |
| - name: validation |
| num_bytes: 2354556.0 |
| num_examples: 75 |
| download_size: 2156044 |
| dataset_size: 2354556.0 |
| - config_name: puzzle |
| features: |
| - name: image |
| dtype: image |
| - name: anl |
| dtype: string |
| - name: pgn |
| dtype: string |
| - name: fen |
| dtype: string |
| - name: label |
| dtype: string |
| - name: id |
| dtype: string |
| splits: |
| - name: validation |
| num_bytes: 5192310.0 |
| num_examples: 200 |
| download_size: 4856203 |
| dataset_size: 5192310.0 |
| - config_name: winner_id |
| features: |
| - name: image |
| dtype: image |
| - name: anl |
| dtype: string |
| - name: pgn |
| dtype: string |
| - name: fen |
| dtype: string |
| - name: label |
| dtype: string |
| - name: id |
| dtype: string |
| splits: |
| - name: validation |
| num_bytes: 6486731 |
| num_examples: 257 |
| download_size: 6026970 |
| dataset_size: 6486731 |
| configs: |
| - config_name: chemistry |
| data_files: |
| - split: validation |
| path: chemistry/validation-* |
| - 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-* |
| - config_name: physics |
| data_files: |
| - split: validation |
| path: physics/validation-* |
| - config_name: puzzle |
| data_files: |
| - split: validation |
| path: puzzle/validation-* |
| - config_name: winner_id |
| data_files: |
| - split: validation |
| path: winner_id/validation-* |
| --- |
| # Dataset Card for IsoBench |
|
|
| <!-- Provide a quick summary of the dataset. --> |
|
|
| 📚 [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 |
|
|
| <!-- Address questions around how the dataset is intended to be used. --> |
| There are 4 major domains: math, algorithm, game, and science. Each domain has several subtasks. |
|
|
| In tatal there are 1,887 samples in the `validation` split with ground-truth labels provided. |
|
|
| The `test` split without labels is coming soon...... |
|
|
| We will show how to load the data for each subtask. |
|
|
| ### TL;DR |
| There are 10 subtasks in total: `math_breakpoint, math_convexity, math_parity, graph_connectivity, graph_maxflow, graph_isomorphism, winner_id, puzzle, chemistry, physics`. |
|
|
| You can load a `subtask` via |
|
|
| ```python |
| from datasets import load_dataset |
| ds_subtask = load_dataset('isobench/IsoBench', subtask, split='validation') |
| ``` |
|
|
|
|
| ### Direct Use |
|
|
| <!-- This section describes suitable use cases for the dataset. --> |
| 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 `domain` 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_parity = load_dataset('isobench/IsoBench', 'math_parity', split='validation') |
| dataset_convexity = load_dataset('isobench/IsoBench', 'math_convexity', split='validation') |
| dataset_breakpoint = 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 |
|
|
| - `image`: a PIL Image feature; |
| - `latex`: a `string` feature, containing the LateX definition of a function; |
| - `code`: a `string` feature, containing the `sympy` definition of a function; |
| - `label`: a `string` feature; |
| - `domain`: a `string` feature or `None`, denoting the domain of a function. This feature is only used for some of the Convexity problems. |
| - `id`: a `string` feature. |
|
|
| ### 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 |
| - `image`: a PIL Image feature |
| - `source_node`: a `unit32` feature, denoting the index of the source node |
| - `source_node_color`: a `string` feature, denoting the color of the `source_node` rendered in the `image` |
| - `sink_node`: a `unit32` feature, denoting the index of the sink node |
| - `sink_node_color`: a `string` feature, denoting the color of the `sink_node` rendered in the `image` |
| - `adjacency_matrix`: a `string` feature, a string of an 2d array representing the adjacency matrix of a graph. The value in entry (i,j) denotes the capacity of flowing from node `i` to node `j`. |
| - `label`: a `uint32` feature |
| - `id`: a `string` feature |
| |
| #### Isomorphism |
| - `image`: a PIL Image feature, consisting of two graphs `G` and `H` |
| - `adjacency_matrix_G`: a `string` feature, a string of an 2d array representing the adjacency matrix of graph `G` |
| - `adjacency_matrix_H`: a `string` feature, a string of an 2d array representing the adjacency matrix of graph `H` |
| - `label`: a `bool` feature, with possible values including `True` (graphs `G` and `H` are isomorphic) and `False` (not isomorphic) |
| - `id`: a `string` feature |
| |
| ### Games |
|
|
| [More Information Needed] |
|
|
| ### Science |
|
|
| [More Information Needed] |
|
|
| ## Citation |
|
|
| <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
|
|
| **BibTeX:** |
|
|
| ```BibTeX |
| @inproceedings{fu2024isobench, |
| title={{I}so{B}ench: Benchmarking Multimodal Foundation Models on Isomorphic Representations}, |
| author={Deqing Fu and Ruohao Guo and Ghazal Khalighinejad and Ollie Liu and Bhuwan Dhingra and Dani Yogatama and Robin Jia and Willie Neiswanger}, |
| booktitle={First Conference on Language Modeling (COLM)}, |
| year={2024}, |
| note={First four authors contributed equally.} |
| } |
| ``` |
|
|
| **Chicago Style:** |
| Deqing Fu<sup>\*</sup>, Ruohao Guo<sup>\*</sup>, Ghazal Khalighinejad<sup>\*</sup>, Ollie Liu<sup>\*</sup>, 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, rguo48@gatech.edu, me@ollieliu.com, ghazal.khalighinejad@duke.edu |
