Update README.md

README.md

@@ -4,7 +4,7 @@ license: apache-2.0
 
 
 # Recurrent Context Compression: Efficiently Expanding the Context Window of LLM
-[[paper]()]
+[[paper](https://arxiv.org/abs/2406.06110)]
 
 
 ## TL;DR
@@ -18,7 +18,7 @@ Due to the limitations of the fine-tuning dataset, the model currently only supp
 
   For the latest information, please follow us on [github](https://github.com/WUHU-G/RCC_Transformer/tree/main)
 
-- [2024/6/10] We released the RCC-Pythia-1.4b model and the [paper]()
+- [2024/6/10] We released the RCC-Pythia-1.4b model and the [paper](https://arxiv.org/abs/2406.06110)
 
 ## Model Overview
 RCC employs an encoder-decoder framework, with both the encoder and decoder weights initialized from a large language model. The trained encoder compresses fixed-length context information into a more compact form, applicable to both instructions and regular text. When context exceeds the fixed length, the encoder performs cyclic compression and concatenates all compressed feature vectors. The decoder uses these compressed vectors as input for final text generation.