Repository: JohnSnowLabs/langtest (language: python)

Issue:
added blog link in readme
# Description
<!-- Thoroughly explain what you are doing and why you are doing it -->

-------------------------------------------------------------------------------------------------
➤ Fixes # (issue)

## Type of change

Please delete options that are not relevant.

- [ ] Bug fix (non-breaking change which fixes an issue)
- [ ] New feature (non-breaking change which adds functionality)
- [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
- [ ] This change requires a documentation update

## Usage
<!-- If applicable: show a code snippet or a command on how to use this new addition -->

## Checklist:
<!--- Go over all the following points, and put an `x` in all the boxes that apply. -->
- [ ] I've added Google style docstrings to my code.
- [ ] I've used `pydantic` for typing when/where necessary.
- [ ] I have linted my code
- [ ] I have added tests to cover my changes.

## Screenshots (if appropriate):


Discussion / hints:

Fix patch:
diff --git a/README.md b/README.md
index f0b2cb486..9e4a6e182 100644
--- a/README.md
+++ b/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/)
 


Test patch:
diff --git a/docs/pages/tests/test.md b/docs/pages/tests/test.md
index 50783cdcf..ba4b84246 100644
--- a/docs/pages/tests/test.md
+++ b/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`                                                                       |
diff --git a/langtest/augmentation/__init__.py b/langtest/augmentation/__init__.py
index 23226f7db..af3f156f8 100644
--- a/langtest/augmentation/__init__.py
+++ b/langtest/augmentation/__init__.py
@@ -331,6 +331,7 @@ def fix(
         training_data: Dict[str, Any],
         output_path: str,
         max_num: int = None,
+        append_original: bool = False,
         *args,
         **kwargs,
     ) -> bool:
@@ -342,6 +343,7 @@ def fix(
             training_data (dict): A dictionary containing the input data for augmentation.
             output_path (str): The path where the augmented data will be saved.
             max_num (int): Maximum number of new samples to generate
+            append_original (bool, optional): If set to True, appends the original data to the augmented data. Defaults to False.
             *args: Variable length argument list.
             **kwargs: Arbitrary keyword arguments.
 
@@ -350,7 +352,7 @@ def fix(
         """
         df = DataFactory(training_data, self.__task)
         data = df.load()
-        new_data = []
+        new_data = data.copy() if append_original else []
         self.__search_results = self.search_sample_results(data)
         if not max_num:
             max_num = max(len(i) for i in self.__search_results.values())
diff --git a/langtest/langtest.py b/langtest/langtest.py
index d6e303f8c..79993fb78 100644
--- a/langtest/langtest.py
+++ b/langtest/langtest.py
@@ -1061,6 +1061,7 @@ def augment(
         custom_proportions: Union[Dict, List] = None,
         export_mode: str = "add",
         templates: Optional[Union[str, List[str]]] = None,
+        append_original: bool = False,
     ) -> "Harness":
         """Augments the data in the input file located at `input_path` and saves the result to `output_path`.
 
@@ -1074,6 +1075,7 @@ def augment(
                                     - 'transformed': Exports only the transformed data, excluding untransformed samples.
                                     Defaults to 'add'.
             templates (Optional[Union[str, List[str]]]):
+            append_original (bool, optional): If set to True, appends the original data to the augmented data. Defaults to False.
 
         Returns:
             Harness: The instance of the class calling this method.
@@ -1123,7 +1125,11 @@ def augment(
             _ = TemplaticAugment(
                 templates=templates,
                 task=self.task,
-            ).fix(training_data=training_data, output_path=save_data_path)
+            ).fix(
+                training_data=training_data,
+                output_path=save_data_path,
+                append_original=append_original,
+            )
 
         else:
             _ = AugmentRobustness(