Update README.md

README.md

@@ -157,7 +157,8 @@ language:
 
 <!-- Provide a quick summary of the dataset. -->
 
-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 the [paper](https://arxiv.org/abs/2404.01266) or [website](https://isobench.github.io).
+📚[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)
@@ -196,13 +197,11 @@ 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.
 
-For more information, be sure to checkout our [paper](https://arxiv.org/abs/2404.01266) and [project website](https://isobench.github.io/)!
-
 #### 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 report in the `xlim` field of each sample. We recommend providing this information as part of the prompt!
+- 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