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README.md

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 <a href="https://github.com/path2generalist/GeneralBench">[📝 Dataset-Github]</a>
 </p>
 
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-</div>
-
-
-<h1 align="center" style="color:#F27E7E"><em>
-Does higher performance across tasks indicate a stronger capability of MLLM, and closer to AGI?
-<br>
-NO! But <b style="color:red">synergy</b> does.
-</em></h1>
-
-
 
-Most current MLLMs predominantly build on the language intelligence of LLMs to simulate the indirect intelligence of multimodality, which is merely extending language intelligence to aid multimodal understanding. While LLMs (e.g., ChatGPT) have already demonstrated such synergy in NLP, reflecting language intelligence, unfortunately, the vast majority of MLLMs do not really achieve it across modalities and tasks.
-
-We argue that the key to advancing towards AGI lies in the synergy effect—a capability that enables knowledge learned in one modality or task to generalize and enhance mastery in other modalities or tasks, fostering mutual improvement across different modalities and tasks through interconnected learning.
-
-
-<div align="center">
-<img src='https://cdn-uploads.huggingface.co/production/uploads/647773a1168cb428e00e9a8f/-Asn68kJGjgqbGqZMrk4E.png'  width=950px>
 </div>
 
+
 ---
+We divide our benchmark into two settings: **`open`** and **`closed`**.
 
-This project introduces **General-Level** and **General-Bench**.
+<!-- This is the **`open benchmark`** of Generalist-Bench, where we release the full ground-truth annotations for all datasets.
+It allows researchers to train and evaluate their models with access to the answers.
 
+If you wish to thoroughly evaluate your model's performance, please use the
+[👉 closed benchmark](https://huggingface.co/datasets/General-Level/General-Bench-Closeset), which comes with detailed usage instructions.
 
-## 🚀🚀🚀 **General-Level**
+Final results will be updated on the [🏆 Leaderboard](https://level.generalist.top). -->
 
-A **`5-scale level evaluation system`** with a new norm for assessing the multimodal generalists (multimodal LLMs/agents). The core is the use of Synergy as the evaluative criterion, categorizing capabilities based on whether MLLMs preserve synergy across comprehension and generation, as well as across multimodal interactions. 
 
+This is the **`Closed benchmark`** of Generalist-Bench, where we release only the question annotations—**without ground-truth answers**—for all datasets.
 
-<div align="center">
-<img src='https://cdn-uploads.huggingface.co/production/uploads/647773a1168cb428e00e9a8f/lnvh5Qri9O23uk3BYiedX.jpeg'>
-</div>
+You can follow the detailed [usage](#-usage) instructions to submit the resuls generate by your own model.
 
+Final results will be updated on the [🏆 Leaderboard](https://level.generalist.top).
 
 
+If you’d like to train or evaluate your model with access to the full answers, please check out the [👉 open benchmark](https://huggingface.co/datasets/General-Level/General-Bench-Openset), where all ground-truth annotations are provided.
 
 
 
-<div align="center">
-<img src='https://cdn-uploads.huggingface.co/production/uploads/647773a1168cb428e00e9a8f/BPqs-3UODQWvjFzvZYkI4.png'  width=1000px>
-</div>
 
 
 
----
-## 🌐🌐🌐 **General-Bench**
-A companion  massive multimodal benchmark dataset, encompasses a broader spectrum of skills, modalities, formats, and capabilities, including over **`700`** tasks and **`325K`** instances. 
 
-<div align="center">
-<img src='https://cdn-uploads.huggingface.co/production/uploads/647773a1168cb428e00e9a8f/d4TIWw3rlWuxpBCEpHYJB.jpeg'>
-<p>Figure: Overview of General-Bench, which covers 145 skills for more than 700 tasks with over 325,800 samples under
-comprehension and generation categories in various modalities</p>
-</div>
 
 
-<div align="center">
-<img src='https://cdn-uploads.huggingface.co/production/uploads/64c139d867eff857ea51caa8/fF3iH95B3QEBvJYwqzZVG.png'>
-<p> Figure: Distribution of various capabilities evaluated in General-Bench.</p>
-</div>
-
-
-<div align="center">
-<img src='https://cdn-uploads.huggingface.co/production/uploads/64c139d867eff857ea51caa8/wQvllVeK-KC3Edp8Zjh-V.png'>
-<p>Figure: Distribution of various domains and disciplines covered by General-Bench.</p>
-</div>
-
+---
 
+##  📕 Table of Contents
 
+- [✨ File Origanization Structure](#fileStructure)
+- [🍟 Usage](#usage)
+- [🌐 General-Bench](#bench)
+  - [🍕 Capabilities and Domians Distribution](#distribution)
+- [🖼️ Image Task Taxonomy](#️imageTaxonomy)
+- [📽️ Video Task Taxonomy](#️videoTaxonomy)
+- [📞 Audio Task Taxonomy](#audioTaxonomy)
+- [💎 3D Task Taxonomy](#3dTaxonomy)
+- [📚 Language Task Taxonomy](#languageTaxonomy)
 
 ---
-
-
-## ✨✨✨ **File Origanization Structure**
+ 
+## ✨✨✨ **File Origanization Structure**<a name="fileStructure" />
 Here is the organization structure of the file system:
 
 ```
@@ -155,13 +130,85 @@ An illustrative example of file formats:
 
 
 
-## Usage
+## 🍟🍟�� Usage<a name="Usage" />
 
 Please download all the files in this repository. We also provide overview.json, which is an example of the format of our dataset.
 
 xxxx
 
 
+---
+## 🌐🌐🌐 **General-Bench**<a name="bench" />
+A companion  massive multimodal benchmark dataset, encompasses a broader spectrum of skills, modalities, formats, and capabilities, including over **`700`** tasks and **`325K`** instances. 
+
+<div align="center">
+<img src='https://cdn-uploads.huggingface.co/production/uploads/647773a1168cb428e00e9a8f/d4TIWw3rlWuxpBCEpHYJB.jpeg'>
+<p> Overview of General-Bench, which covers 145 skills for more than 700 tasks with over 325,800 samples under
+comprehension and generation categories in various modalities</p>
+</div>
+
+### 🍕🍕🍕 Capabilities and Domians Distribution<a name="distribution" />
+
+<div align="center">
+<img src='https://cdn-uploads.huggingface.co/production/uploads/64c139d867eff857ea51caa8/fF3iH95B3QEBvJYwqzZVG.png'>
+<p> Distribution of various capabilities evaluated in General-Bench.</p>
+</div>
+
+
+<div align="center">
+<img src='https://cdn-uploads.huggingface.co/production/uploads/64c139d867eff857ea51caa8/wQvllVeK-KC3Edp8Zjh-V.png'>
+<p>Distribution of various domains and disciplines covered by General-Bench.</p>
+</div>
+
+
+## 🖼️ Image Task Taxonomy<a name="imageTaxonomy" />
+<div align="center">
+<img src='https://cdn-uploads.huggingface.co/production/uploads/64c139d867eff857ea51caa8/2QYihQRhZ5C9K5IbukY7R.png'>
+<p>Taxonomy and hierarchy of data in terms of Image modality.</p>
+</div>
+
+
+## 📽️ Video Task Taxonomy<a name="videoTaxonomy" />
+
+<div align="center">
+<img src='https://cdn-uploads.huggingface.co/production/uploads/64c139d867eff857ea51caa8/A7PwfW5gXzstkDH49yIG5.png'>
+<p>Taxonomy and hierarchy of data in terms of Video modality.</p>
+</div>
+
+
+
+## 📞 Audio Task Taxonomy<a name="audioTaxonomy" />
+
+
+
+<div align="center">
+<img src='https://cdn-uploads.huggingface.co/production/uploads/64c139d867eff857ea51caa8/e-QBvBjeZy8vmcBjAB0PE.png'>
+<p>Taxonomy and hierarchy of data in terms of Audio modality.</p>
+</div>
+
+## 💎 3D Task Taxonomy<a name="3dTaxonomy" />
+
+
+<div align="center">
+<img src='https://cdn-uploads.huggingface.co/production/uploads/64c139d867eff857ea51caa8/EBXb-wyve14ExoLCgrpDK.png'>
+<p>Taxonomy and hierarchy of data in terms of 3D modality.</p>
+</div>
+
+
+## 📚 Language Task Taxonomy<a name="languageTaxonomy" />
+
+<div align="center">
+<img src='https://cdn-uploads.huggingface.co/production/uploads/64c139d867eff857ea51caa8/FLfk3QGdYb2sgorKTj_LT.png'>
+<p>Taxonomy and hierarchy of data in terms of Language modality.</p>
+</div>
+
+
+
+
+---
+
+
+
 
 ## 🚩 **Citation**