Update README.md

README.md

@@ -9,12 +9,12 @@ license: bsd-3-clause
 [CodeT5+](https://github.com/salesforce/CodeT5/tree/main/CodeT5+) is a new family of open code large language models with an encoder-decoder architecture that can flexibly operate in different modes (i.e. _encoder-only_, _decoder-only_, and _encoder-decoder_) to support a wide range of code understanding and generation tasks. 
 It is introduced in the paper:
 
-[CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://github.com/salesforce/CodeT5/CodeT5+)
+[CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://arxiv.org/pdf/2305.07922.pdf)
 by [Yue Wang](https://yuewang-cuhk.github.io/)\*, [Hung Le](https://sites.google.com/view/henryle2018/home?pli=1)\*, [Akhilesh Deepak Gotmare](https://akhileshgotmare.github.io/), [Nghi D.Q. Bui](https://bdqnghi.github.io/), [Junnan Li](https://sites.google.com/site/junnanlics), [Steven C.H. Hoi](https://sites.google.com/view/stevenhoi/home) (* indicates equal contribution).
 
 Compared to the original CodeT5 family (CodeT5-base: `220M`, CodeT5-large: `770M`), CodeT5+ is pretrained with a diverse set of pretraining tasks including _span denoising_, _causal language modeling_, _contrastive learning_, and _text-code matching_ to learn rich representations from both unimodal code data and bimodal code-text data. 
 Additionally, it employs a simple yet effective _compute-efficient pretraining_ method to initialize the model components with frozen off-the-shelf LLMs such as [CodeGen](https://github.com/salesforce/CodeGen) to efficiently scale up the model (i.e. `2B`, `6B`, `16B`), and adopts a "shallow encoder and deep decoder" architecture. 
-Furthermore, it is instruction-tuned to align with natural language instructions (see [InstructCodeT5+ 16B](https://github.com/salesforce/CodeT5/tree/main/CodeT5+)) following [Code Alpaca](https://github.com/sahil280114/codealpaca).  
+Furthermore, it is instruction-tuned to align with natural language instructions (see our InstructCodeT5+ 16B) following [Code Alpaca](https://github.com/sahil280114/codealpaca).  
 
 ## How to use
 
@@ -54,7 +54,7 @@ Specifically, CodeT5+ yields substantial performance gains on many downstream ta
 8 text-to-code retrieval tasks (+3.2 avg. MRR), 2 line-level code completion tasks (+2.1 avg. Exact Match), and 2 retrieval-augmented code generation tasks (+5.8 avg. BLEU-4). 
 In 2 math programming tasks on MathQA-Python and GSM8K-Python, CodeT5+ models of below billion-parameter sizes significantly outperform many LLMs of up to 137B parameters. 
 Particularly, in the zero-shot text-to-code generation task on HumanEval benchmark, InstructCodeT5+ 16B sets new SoTA results of 35.0% pass@1 and 54.5% pass@10 against other open code LLMs, even surpassing the closed-source OpenAI code-cushman-001 mode
-Please refer to the [paper](https://github.com/salesforce/CodeT5/tree/main/CodeT5+) for more details.
+Please refer to the [paper](https://arxiv.org/pdf/2305.07922.pdf) for more details.
 
 
 ## BibTeX entry and citation info