Merge pull request #840 from JohnSnowLabs/chore/website-updates

added blog link in readme

README.md

@@ -114,7 +114,10 @@ You can check out the following langtest articles:
 | [**Elevate Your NLP Models with Automated Data Augmentation for Enhanced Performance**](https://medium.com/john-snow-labs/elevate-your-nlp-models-with-automated-data-augmentation-for-enhanced-performance-71aa7812c699) | In this article, we discuss how automated data augmentation may supercharge your NLP models and improve their performance and how we do that using  LangTest. |
 | [**Mitigating Gender-Occupational Stereotypes in AI: Evaluating Models with the Wino Bias Test through Langtest Library**](https://www.johnsnowlabs.com/mitigating-gender-occupational-stereotypes-in-ai-evaluating-language-models-with-the-wino-bias-test-through-the-langtest-library/) | In this article, we discuss how we can test the "Wino Bias” using LangTest. It specifically refers to testing biases arising from gender-occupational stereotypes. |
 | [**Automating Responsible AI: Integrating Hugging Face and LangTest for More Robust Models**](https://www.johnsnowlabs.com/automating-responsible-ai-integrating-hugging-face-and-langtest-for-more-robust-models/) | In this article, we have explored the integration between Hugging Face, your go-to source for state-of-the-art NLP models and datasets, and LangTest, your NLP pipeline’s secret weapon for testing and optimization. |
-
+| [**Detecting and Evaluating Sycophancy Bias: An Analysis of LLM and AI Solutions**](https://medium.com/john-snow-labs/detecting-and-evaluating-sycophancy-bias-an-analysis-of-llm-and-ai-solutions-ce7c93acb5db) | In this blog post, we discuss the pervasive issue of sycophantic AI behavior and the challenges it presents in the world of artificial intelligence. We explore how language models sometimes prioritize agreement over authenticity, hindering meaningful and unbiased conversations. Furthermore, we unveil a potential game-changing solution to this problem, synthetic data, which promises to revolutionize the way AI companions engage in discussions, making them more reliable and accurate across various real-world conditions. |
+| [**Unmasking Language Model Sensitivity in Negation and Toxicity Evaluations**](https://medium.com/john-snow-labs/unmasking-language-model-sensitivity-in-negation-and-toxicity-evaluations-f835cdc9cabf) | In this blog post, we delve into Language Model Sensitivity, examining how models handle negations and toxicity in language. Through these tests, we gain insights into the models' adaptability and responsiveness, emphasizing the continuous need for improvement in NLP models. |
+| [**Unveiling Bias in Language Models: Gender, Race, Disability, and Socioeconomic Perspectives**](https://medium.com/john-snow-labs/unveiling-bias-in-language-models-gender-race-disability-and-socioeconomic-perspectives-af0206ed0feb) | In this blog post, we explore bias in Language Models, focusing on gender, race, disability, and socioeconomic factors. We assess this bias using the CrowS-Pairs dataset, designed to measure stereotypical biases. To address these biases, we discuss the importance of tools like LangTest in promoting fairness in NLP systems. |
+| [**Unmasking the Biases Within AI: How Gender, Ethnicity, Religion, and Economics Shape NLP and Beyond**](https://medium.com/@chakravarthik27/cf69c203f52c) | In this blog post, we tackle AI bias on how Gender, Ethnicity, Religion, and Economics Shape NLP systems. We discussed strategies for reducing bias and promoting fairness in AI systems. |
 > **Note**
 > To checkout all blogs, head over to [Blogs](https://www.johnsnowlabs.com/responsible-ai-blog/)
 

docs/pages/tests/test.md

@@ -111,8 +111,10 @@ The following tables give an overview of the different categories and tests.
 | [Security](security)             | [prompt_injection_attack](security#prompt_injection_attack)                                                     | `security`                                                                          |
 | [Disinformation](disinformation) | [Narrative Wedging](disinformation#narrative_wedging)                                                           | `disinformation-test`                                                               |
 | [Factuality](factuality)         | [Order Bias](factuality#order_bias)                                                                             | `factuality-test`                                                                   |
-| [Sensitivity](Sensitivity)       | [Negation](Sensitivity#negation)                                                                                | `Sensitivity-test`                                                                  |
-| [Sensitivity](Sensitivity)       | [Toxicity](Sensitivity#toxicity)                                                                                | `Sensitivity-test`                                                                  |
+| [Sensitivity](sensitivity)       | [Negation](sensitivity#negation)                                                                                | `sensitivity-test`                                                                  |
+| [Sensitivity](sensitivity)       | [Toxicity](sensitivity#toxicity)                                                                                | `sensitivity-test`                                                                  |
+| [Sycophancy](sycophancy)       | [Sycophancy Math](sycophancy#sycophancy_math)                                                                                | `sycophancy-test`                                                                  |
+| [Sycophancy](sycophancy)       | [Sycophancy NLP](sycophancy#sycophancy_nlp)                                                                                | `sycophancy-test`                                                                  |
 | [Legal](Legal)                   | [legal-support](legal#legal-support)                                                                            | `legal-tests`                                                                       |
 | [Wino Bias](wino-bias)           | [gender-occupational-stereotype](wino-bias#gender-occupational-stereotype)                                      | `wino-bias`                                                                         |
 | [Crows Pairs](crows-pairs)       | [common-stereotypes](crows-pairs#common-stereotypes)                                                            | `crows-pairs`                                                                       |