Improve language tag

README.md

@@ -1,27 +1,41 @@
----
-license: mit
-base_model:
-- Qwen/Qwen2.5-3B-Instruct
----
-# DeepRetrieval
-## Overview
-
-DeepRetrieval is a novel approach that uses reinforcement learning (RL) to train Large Language Models (LLMs) for query generation without requiring supervised data. Instead of relying on expensive human-annotated or distilled reference queries, DeepRetrieval enables LLMs to learn through direct trial and error, using retrieval metrics as rewards.
-## Key Features
-
-- **No Supervision Required**: Eliminates the need for expensive human-annotated or distilled reference queries
-- **RL-Based Framework**: Uses reinforcement learning to optimize query generation directly for retrieval performance
-- **State-of-the-Art Performance**: Achieves remarkable results across diverse retrieval tasks
-
-Please view our [GitHub page](https://github.com/pat-jj/DeepRetrieval) for instructions.
-
-[DeepRetrieval Paper](arxiv.org/abs/2503.00223)
-```
-@article{jiang2025deepretrievalhackingrealsearch,
-      title={DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning}, 
-      author={Pengcheng Jiang and Jiacheng Lin and Lang Cao and Runchu Tian and SeongKu Kang and Zifeng Wang and Jimeng Sun and Jiawei Han},
-      year={2025},
-      journal = {arXiv preprint arXiv: 2503.00223},
-      url={https://arxiv.org/abs/2503.00223}
-  }
+---
+license: mit
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
+language:
+- zho
+- eng
+- fra
+- spa
+- por
+- deu
+- ita
+- rus
+- jpn
+- kor
+- vie
+- tha
+- ara
+---
+# DeepRetrieval
+## Overview
+
+DeepRetrieval is a novel approach that uses reinforcement learning (RL) to train Large Language Models (LLMs) for query generation without requiring supervised data. Instead of relying on expensive human-annotated or distilled reference queries, DeepRetrieval enables LLMs to learn through direct trial and error, using retrieval metrics as rewards.
+## Key Features
+
+- **No Supervision Required**: Eliminates the need for expensive human-annotated or distilled reference queries
+- **RL-Based Framework**: Uses reinforcement learning to optimize query generation directly for retrieval performance
+- **State-of-the-Art Performance**: Achieves remarkable results across diverse retrieval tasks
+
+Please view our [GitHub page](https://github.com/pat-jj/DeepRetrieval) for instructions.
+
+[DeepRetrieval Paper](arxiv.org/abs/2503.00223)
+```
+@article{jiang2025deepretrievalhackingrealsearch,
+      title={DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement Learning}, 
+      author={Pengcheng Jiang and Jiacheng Lin and Lang Cao and Runchu Tian and SeongKu Kang and Zifeng Wang and Jimeng Sun and Jiawei Han},
+      year={2025},
+      journal = {arXiv preprint arXiv: 2503.00223},
+      url={https://arxiv.org/abs/2503.00223}
+  }
 ```