Update README.md

README.md

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 As a part of our research efforts to make LLMs safer, we created **Starling**. It is obtained by fine-tuning Vicuna-7B on [**HarmfulQA**](https://huggingface.co/datasets/declare-lab/HarmfulQA), a ChatGPT-distilled dataset that we collected using the Chain of Utterances (CoU) prompt. More details are in our paper [**Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment**](https://arxiv.org/abs/2308.09662)
 
-<img src="https://declare-lab.net/assets/images/logos/starling-final.png" alt="Image" width="100" height="100">
+<img src="https://declare-lab.github.io/assets/images/logos/starling-final.png" alt="Image" width="100" height="100">
 
 Experimental results on several safety benchmark datasets indicate that **Starling** is a safer model compared to the baseline model, Vicuna.
 
-<img src="https://declare-lab.net/assets/images/logos/method.png" alt="Image" width="1000" height="335">
+<img src="https://declare-lab.github.io/assets/images/logos/method.png" alt="Image" width="1000" height="335">
 
 <h2>Experimental Results</h2>
 
@@ -20,7 +20,7 @@ Compared to Vicuna, **Avg. 5.2% reduction in Attack Success Rate** (ASR) on Dang
 
 Compared to Vicuna, **Avg. 3-7% improvement in HHH score** measured on BBH-HHH benchmark.**
 
-<img src="https://declare-lab.net/assets/images/logos/starling-results.png" alt="Image" width="1000" height="335">
+<img src="https://declare-lab.github.io/assets/images/logos/starling-results.png" alt="Image" width="1000" height="335">
 
 TruthfulQA (MC2): **48.90 vs Vicuna's 47.00**
 
@@ -32,13 +32,13 @@ BBH (3-shot): **33.47 vs Vicuna's 33.05**
 
 This jailbreak prompt (termed as Chain of Utterances (CoU) prompt in the paper) shows a 65% Attack Success Rate (ASR) on GPT-4 and 72% on ChatGPT.
 
-<img src="https://declare-lab.net/assets/images/logos/jailbreakprompt_main_paper.png" alt="Image" width="1000" height="1000">
+<img src="https://declare-lab.github.io/assets/images/logos/jailbreakprompt_main_paper.png" alt="Image" width="1000" height="1000">
 
 <h2>HarmfulQA Data Collection</h2>
 
 We also release our **HarmfulQA** dataset with 1,960 harmful questions (converting 10 topics-10 subtopics) for red-teaming as well as conversations based on them used in model safety alignment, more details [**here**](https://huggingface.co/datasets/declare-lab/HarmfulQA). The following figure describes the data collection process.
 
-<img src="https://declare-lab.net/assets/images/logos/data_gen.png" alt="Image" width="1000" height="1000">
+<img src="https://declare-lab.github.io/assets/images/logos/data_gen.png" alt="Image" width="1000" height="1000">
 
 _Note: This model is referred to as Starling (Blue) in the paper. We shall soon release Starling (Blue-Red) which was trained on harmful data using an objective function that helps the model learn from the red (harmful) response data._