LLMs Every Data Scientist Should Know

Different LLMs serve distinct purposes, from search engines and chatbots to data generation and multi-modal learning. As a Data Scientist, you don’t need to learn all the LLMs, as there is a different role for that, i.e., LLM Engineer. So, in this article, I’ll take you through all the LLMs that every Data Scientist should know based on their categories.

LLMs Every Data Scientist Should Know

Let’s go through all the LLMs every Data Scientist should know across four key categories: Auto-Encoding Models, Auto-Regressive Models, Sequence-to-Sequence Models, and Multi-Modal Models.

Auto-Encoding Models

Auto-encoding models are bidirectional transformers trained using masked language modelling (MLM). These models are crucial for tasks requiring deep contextual understanding, such as text classification, search ranking, and information retrieval.

These models are best for applications needing strong text comprehension rather than generation. Here are the essential LLMs you should know based on auto-encoding models:

1. BERT: for applications like Search engines (Google Search), question answering, sentiment analysis, and named entity recognition (NER).
2. RoBERTa: for applications like Document classification, recommendation systems, and legal/financial text analysis.

Here are some learning resources you can follow:

1. Text Classification using BERT
2. Sentiment Analysis using RoBERTa

Auto-Regressive Models

Auto-regressive models generate text token-by-token in a left-to-right manner. They are used in chatbots, creative writing, and AI-assisted programming. These models are essential for any application requiring dynamic text generation and reasoning.

Here are the essential LLMs you should know based on auto-regressive models:

1. GPT-4: for applications like Chatbots, content generation, programming assistance, and research.
2. LLaMA 2: for applications like Open-source conversational AI, summarization, and knowledge extraction.

Here are some learning resources you can follow:

1. Fine-tuning LLaMa2
2. A Guide to Fine-tune GPT Models

Sequence-to-Sequence (Seq2Seq) Models

Seq2Seq models use an encoder-decoder structure, making them powerful for applications requiring structured input-to-output transformations. These models are essential for structured NLP tasks requiring input transformation.

Here are the essential LLMs you should know based on Seq2Seq models:

1. T5: for applications like Machine translation, text summarization, question answering, and data augmentation.
2. BART: for applications like News summarization, dialogue generation, and text simplification.

Here are some learning resources you can follow:

1. Fine-tuning T5
2. Distributed Training using BART

Multi-Modal Models

Multi-modal models process multiple types of data (text, images, and audio), enabling applications like AI vision, speech-to-text, and AI-assisted creativity. These models are vital for bridging the gap between text, vision, and speech AI applications.

Here are the essential LLMs you should know based on multi-modal models:

1. CLIP: for applications like AI art tools, image tagging and captioning, and zero-shot classification.
2. GPT-4V: for applications like AI-powered assistants, document analysis, and image captioning.

Here are some learning resources you can follow:

1. Image Caption Recommendation System with CLIP
2. A Guide to GPT-4V

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Summary

So, here are all the LLMs every Data Scientist should know:

1. BERT: Search engines, question answering, sentiment analysis, NER.
2. RoBERTa: Document classification, recommendation systems, legal/financial text analysis.
3. GPT-4: Chatbots, content generation, programming, research.
4. LLaMA 2: Conversational AI, summarization, knowledge extraction.
5. T5: Translation, summarization, question answering, data augmentation.
6. BART: News summarization, dialogue, text simplification.
7. CLIP: AI art, image tagging, captioning, zero-shot classification.
8. GPT-4V: AI assistants, document analysis, image captioning.

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