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@@ -84,10 +84,31 @@ Langtest comes with different datasets to test your models, covering a wide rang
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|[**BBQ**](https://arxiv.org/abs/2110.08193)| Evaluate how your model responds to questions in the presence of social biases against protected classes across various social dimensions. Assess biases in model outputs with both under-informative and adequately informative contexts, aiming to promote fair and unbiased question-answering models. |[](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/dataset-notebooks/BBQ_dataset.ipynb)|
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|[**XSum**](https://aclanthology.org/D18-1206/)| Evaluate your model's ability to generate concise and informative summaries for long articles with the XSum dataset. It consists of articles and corresponding one-sentence summaries, offering a valuable benchmark for text summarization models. |[](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/dataset-notebooks/XSum_dataset.ipynb)|
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|[**Real Toxicity Prompts**](https://aclanthology.org/2020.findings-emnlp.301/) | Evaluate your model's accuracy in recognizing and handling toxic language with the Real Toxicity Prompts dataset. It contains real-world prompts from online platforms, ensuring robustness in NLP models to maintain safe environments. | [](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/OpenAI_QA_Testing_Notebook.ipynb)
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|[**LogiQA**](https://aclanthology.org/2020.findings-emnlp.301/) | Evaluate your model's accuracy on Machine Reading Comprehension with Logical Reasoning questions. | [](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/dataset-notebooks/LogiQA_dataset.ipynb)
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|[**BigBench Abstract narrative understanding**](https://arxiv.org/abs/2206.04615) | Evaluate your model's performance in selecting the most relevant proverb for a given narrative. | [](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/dataset-notebooks/Bigbench_dataset.ipynb)
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|[**BigBench Causal Judgment**](https://arxiv.org/abs/2206.04615) | Evaluate your model's performance in measuring the ability to reason about cause and effect. | [](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/dataset-notebooks/Bigbench_dataset.ipynb)
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|[**BigBench DisambiguationQA**](https://arxiv.org/abs/2206.04615) | Evaluate your model's performance on determining the interpretation of sentences containing ambiguous pronoun references.| [](hhttps://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/dataset-notebooks/Bigbench_dataset.ipynb)
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|[**BigBench DisflQA**](https://arxiv.org/abs/2206.04615) | Evaluate your model's performance in picking the correct answer span from the context given the disfluent question. | [](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/dataset-notebooks/Bigbench_dataset.ipynb)
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|[**ASDiv**](https://arxiv.org/abs/2106.15772) | Evaluate your model's ability answer questions based on Math Word Problems. | [](https://colab.research.google.com/github/JohnSnowLabs/langtest/blob/main/demo/tutorials/llm_notebooks/dataset-notebooks/ASDiv_dataset.ipynb)
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> **Note**
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> For usage and documentation, head over to [langtest.org](https://langtest.org/docs/pages/docs/data#question-answering)
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## Blog
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You can check out the following langtest articles:
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| Blog | Description |
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|------|-------------|
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|[**Automatically Testing for Demographic Bias in Clinical Treatment Plans Generated by Large Language Models**](https://medium.com/john-snow-labs/automatically-testing-for-demographic-bias-in-clinical-treatment-plans-generated-by-large-language-ffcf358b6092)| Helps in understanding and testing demographic bias in clinical treatment plans generated by LLM. |
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|[**LangTest: Unveiling & Fixing Biases with End-to-End NLP Pipelines**](https://www.johnsnowlabs.com/langtest-unveiling-fixing-biases-with-end-to-end-nlp-pipelines/)| The end-to-end language pipeline in LangTest empowers NLP practitioners to tackle biases in language models with a comprehensive, data-driven, and iterative approach. |
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|[**Beyond Accuracy: Robustness Testing of Named Entity Recognition Models with LangTest**](To be Published Soon) | While accuracy is undoubtedly crucial, robustness testing takes natural language processing (NLP) models evaluation to the next level by ensuring that models can perform reliably and consistently across a wide array of real-world conditions. |
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|[**Elevate Your NLP Models with Automated Data Augmentation for Enhanced Performance**](To be Published Soon) | 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. |
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> **Note**
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> To checkout all blogs, head over to [Blogs](https://www.johnsnowlabs.com/responsible-ai-blog/)
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