Update README.md

README.md

@@ -10,8 +10,26 @@ Experimental results on several safety benchmark datasets indicate that **Starli
 
 <h2>Experimental Results</h2>
 
+**Compared to Vicuna, Avg. 5.2% reduction in Attack Success Rate (ASR) on DangerousQA and HarmfulQA using three different prompts.**
+
+**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">
 
-TruthfulQA (MC2): 48.90 vs Vicuna's 47.00
+**TruthfulQA (MC2): 48.90 vs Vicuna's 47.00**
+
+**MMLU (5-shot): 46.69 vs Vicuna's 47.18**
+
+**BBH (3-shot): 33.47 vs Vicuna's 33.05**
+
+<h2>Jailbreak Prompt for harmfulness eval using Red Eval as reported in the paper</h2>
+
+**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/djailbreakprompt_main_paper.png" alt="Image" width="1000" height="1000">
+
+<h2>HarmfulQA Data Collection</h2>
+
+<img src="https://declare-lab.net/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 model learn from the negative data.