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LLMs, Prompting, NLP and Annotation

This repository houses several Practical Data Science assignements relating to LLM Prompting (use cases, modern prompting techniques, fail conditions).

The repository is organized as follows:

  1. ChatGPT prompting fail cases where we explore two cases of ChatGPT code generation failing.

  2. LLM prompting for annotation where we leverage LLMs to help us in a difficult annotation task, which we then handle by using pandas.

  3. Annotation analysis where we analyze the clustering between different annotators.

  4. Secure-GPT a group presentation for a new LLM use-case to make the internet safer - includes fake data generation for demonstration purposes.

  5. Webscraping where we create a webscraper for a Greek gaming forum.

  6. Greek Embedding Plot, in which we create an interquartile range for all the posts in a Greeklish2Greek dataset.

  7. Greek Embedding Creation in which we create and test our own Greek Embeddings.

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Practical Data Science assignments relating to LLMs, prompting and annotation tasks.

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