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What are some ethical implications of Large Language models?

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Ethical consideration in LLMs In this article, we're going to address the ethical challenges surrounding the usage of large language models, including potential biases and privacy issues.  Bias in AI system: AI systems, particularly, will deliver biased results. There's no other way to get around it. Search engine technology, for example. It's not neutral, processes a large amount of data, and prioritizes results based on clicks and user preferences. So, a search engine can become essentially an echo chamber. And you can see that it will uphold certain biases of the real world, potentially because looking at the clicks and the user preferences of a majority of users using that search engine. And this is how we can come up with prejudices and stereotypes that are furthered online because of these systems that are in place. So, when we talk about bias in AI, it could be something like as simple as a gender bias, right? So, gender biases should be avoided, but it can't sho...
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Introduction to the fine tuning in Large Language Models

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Fine tuning in LLM In this article, we're going to delve into the techniques used to adapt a generic pre-trained model for a specific task or application. Now, pre-trained language models can do really impressive stuff just immediately off the shelf, including things like text generation, summarization, and even providing levels of code. But LLMs are not one-size-fits-all as you can imagine and maybe I want to specialize it even further after the model has been trained. I'd like to make it really good at answering questions based on certain topics such as medicine or travel or code or something. This is what the idea of fine-tuning comes in.  Methods used in Fine tuning: Fine-tuning a large language model refers to the process of taking a pre-trained language model or a model that's already been trained on a large dataset and further training it on a smaller task-specific dataset. Now this process will allow the model to adapt and specialize its capability. And if we take t...
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What are the impacts of data size and its quality on LLMs?

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Data on LLMs In this article, I'm going to explore the impact of data size and quality and how it can affect a large language model. So, we understand that these large language models are really good at text generation and they're great at being just deep learning models, they're transformers, right? At their very core, they're just like any other AI or machine learning model and they just happen to be good at natural language generation. Now these models, since they're able to learn the complexity of human language, their potential is really immense. You can almost tell that these applications are going to be developed for all sorts of things.  Understanding Data Quality: Now, even depending on let's say if we're trying to do something like text generation or text image generation or image captioning or all sorts of sectors from finance to telecom and healthcare, they're all going to be using these tools. Then the question is, is, well what data should ...
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