[doc] add imbue

README.md

@@ -21,7 +21,7 @@ Training
 Utilization
 
 - **Blazingly Fast:** By managing the KV Cache of prefixes, we can speed up local inference by up to 6x 🚀.
-- **Comprehensive Evaluation:** 56+ commonly used [datasets](https://github.com/RUCAIBox/LLMBox/blob/main/docs/utilization/supported-datasets.md) and benchmarks in evaluating LLMs.
+- **Comprehensive Evaluation:** 59+ commonly used [datasets](https://github.com/RUCAIBox/LLMBox/blob/main/docs/utilization/supported-datasets.md) and benchmarks in evaluating LLMs.
 - **Evaluation Methods:** Accurately reproduce results from original papers of OpenAI, LLaMA, Mistral, and other models.
 - **In-Context Learning:** We support various ICL strategies, including [`KATE`](https://aclanthology.org/2022.deelio-1.10/), [`GlobalE`](https://aclanthology.org/2022.acl-long.556/), and [`APE`](https://arxiv.org/abs/2211.01910).
 - **Chain-of-Thought:** For some datasets, we support three types of CoT evaluation: `base`, [`least-to-most`](https://arxiv.org/abs/2205.10625), and [`pal`](https://arxiv.org/abs/2211.10435).
@@ -140,7 +140,7 @@ For more details, view the [training](https://github.com/RUCAIBox/LLMBox/tree/ma
 
 We provide a broad support on Huggingface models (e.g. `LLaMA-3`, `Mistral`, or the model you are building on), OpenAI, Anthropic, QWen and other OpenAI-compatible models for further utilization. Full list of model backends: [here](https://github.com/RUCAIBox/LLMBox/tree/main/utilization#supported-models).
 
-Currently a total of 56+ commonly used datasets are supported, including: `HellaSwag`, `MMLU`, `GSM8K`, `GPQA`, `AGIEval`, `CEval`, and `CMMLU`. Full list of datasets: [here](https://github.com/RUCAIBox/LLMBox/blob/main/docs/utilization/supported-datasets.md).
+Currently a total of 59+ commonly used datasets are supported, including: `HellaSwag`, `MMLU`, `GSM8K`, `GPQA`, `AGIEval`, `CEval`, and `CMMLU`. Full list of datasets: [here](https://github.com/RUCAIBox/LLMBox/blob/main/docs/utilization/supported-datasets.md).
 
 ```bash
 CUDA_VISIBLE_DEVICES=0 python inference.py \

docs/utilization/supported-datasets.md

@@ -1,16 +1,22 @@
 # Supported Datasets of LLMBox
 
-We currently support 56+ commonly used datasets for LLMs. Each dataset is either a multiple-choice dataset or a generation dataset.
+We currently support 59+ commonly used datasets for LLMs.
+
+## Understanding Evaluation Type
+
+Each dataset is either a multiple-choice dataset or a generation dataset. You can find the difference between them at [here](https://github.com/RUCAIBox/LLMBox/tree/main/utilization#dataset-arguments)
+
+## Understanding Subsets
 
 Some datasets have multiple subsets. For example, Massive Multitask Language Understanding (`mmlu`) dataset contains 57 different subsets categorized into four categories: `stem`, `social_sciences`, `humanities`, and `other`.
 
 While some other dataset is a subset of another dataset. For example, Choice Of Plausible Alternatives (`copa`) is a subset of `super_glue`.
 
 See how to [load datasets with subsets](https://github.com/RUCAIBox/LLMBox/tree/main/docs/utilization/how-to-load-datasets-with-subsets.md).
 
-Some datasets support Chain-of-Thought reasoning. For example, Grade School Math 8K (`gsm8k`) supports three types of CoT: `base`, `least_to_most`, and `pal`.
+## Understanding CoT
 
-You can find the supported datasets in the following table.
+Some datasets support Chain-of-Thought reasoning. For example, Grade School Math 8K (`gsm8k`) supports three types of CoT: `base`, `least_to_most`, and `pal`.
 
 ## Supported Datasets
 
@@ -177,7 +183,7 @@ You can find the supported datasets in the following table.
   <tr>
       <td>Google-Proof Q&A (<code>GPQA</code>)</td>
       <td><code>gpqa_main</code> (default), <code>gpqa_extended</code>, ...</td>
-      <td>MultipleChoiceDataset</td>
+      <td>MultipleChoice</td>
       <td>✅</td>
       <td></td>
   </tr>
@@ -216,6 +222,27 @@ You can find the supported datasets in the following table.
       <td></td>
       <td></td>
   </tr>
+  <tr>
+      <td><a href="https://huggingface.co/datasets/imbue/code-comprehension">Imbue Code Comprehension</a> (<code>imbue_code</code>)</td>
+      <td>/</td>
+      <td>MultipleChoice</td>
+      <td></td>
+      <td></td>
+  </tr>
+  <tr>
+      <td><a href="https://huggingface.co/datasets/imbue/high_quality_private_evaluations">Imbue High Quality Private Evaluations</a> (<code>imbue_private</code>)</td>
+      <td>/</td>
+      <td>MultipleChoice</td>
+      <td></td>
+      <td></td>
+  </tr>
+  <tr>
+      <td><a href="https://huggingface.co/datasets/imbue/high_quality_public_evaluations">Imbue High Quality Public Evaluations</a> (<code>imbue_public</code>)</td>
+      <td>/</td>
+      <td>MultipleChoice</td>
+      <td></td>
+      <td></td>
+  </tr>
   <tr>
       <td>LAnguage Modeling Broadened to Account for Discourse Aspects (<code>lambada</code>)</td>
       <td><code>default</code> (default), <code>de</code>, ... (source: <i>EleutherAI/lambada_openai</i>)</td>