All About LLMs

We’re excited to offer future sessions on this topic! If you’re interested in attending our upcoming seminar with updated content, sign up below, and we’ll notify you when the event is scheduled.

This event provides an overview of the most prominent Large Language Models (LLMs), their architectures, and the unique features that make them powerful.

Success Factors of LLMs

Understanding the key factors behind the effectiveness and intelligence of Large Language Models.

1. Scaling Laws
   Learn how the size of LLMs, including the number of parameters and data used, directly impacts their performance and capabilities. Explore the relationship between scale and accuracy, as well as diminishing returns.
2. Emerging Abilities of LLMs
   Discover the unique, often unexpected abilities that arise in LLMs as they grow larger, such as in-context learning, few-shot prompting, and more advanced reasoning skills.
3. Impact of Scaling on Emerging Abilities of LLMs
   Understand how scaling LLMs leads to the development of new capabilities, including handling more complex language tasks and achieving higher accuracy with less training data.
4. Techniques that Enabled LLMs to Have Emerging Abilities
   Explore the underlying techniques that allow LLMs to develop their emerging abilities, including self-supervised learning, multi-task training, and transformer-based architectures.

LLM Resources

A comprehensive overview of the various resources available for working with LLMs, from open-source and closed-source models to data and libraries.

1. Open-Source LLMs – Smaller Models
   Learn about smaller-scale open-source models like GPT-Neo and DistilBERT that offer efficient alternatives to larger models, ideal for smaller applications and research projects.
2. Closed-Source LLMs – Smaller Models
   Explore smaller, proprietary LLMs like GPT-3 (restricted versions) or Microsoft’s Turing-NLG, which are optimized for specific commercial applications.
3. Open-Source LLMs – Larger Models
   Delve into larger open-source models such as GPT-J and GPT-NeoX, which offer high-quality performance and are backed by extensive community-driven development.
4. Closed-Source LLMs – Larger Models
   Understand the capabilities of large-scale proprietary LLMs like OpenAI’s GPT-4 and Google’s PaLM, which are at the cutting edge of AI but have limited public access.
5. Commonly Used Corpora for Pre-Training
   Explore the large-scale datasets used to pre-train LLMs, including Common Crawl, Wikipedia, and large web datasets that provide models with vast linguistic and contextual knowledge.
6. Commonly Used Corpora for Fine-Tuning
   Learn about the specialized datasets used to fine-tune LLMs for domain-specific tasks, such as SQuAD for question answering, or CoNLL for named entity recognition.
7. Libraries Available for Developing LLMs
   Discover the popular libraries and frameworks available for developing and deploying LLMs, including Hugging Face’s Transformers, PyTorch, and TensorFlow.
8. Pre-Training Pipeline of LLMs
   Get an in-depth look at the pre-training pipeline of LLMs, covering data collection, tokenization, and the training of models on large-scale datasets to develop foundational language understanding.
9. Fine-Tuning of LLMs
   Learn how to fine-tune pre-trained LLMs for specific tasks, such as sentiment analysis, summarization, or machine translation, by adapting them to smaller, task-specific datasets.

Interested in attending this event?

Register on this page, and we’ll notify you when we schedule the event!

Register