Introduction to LLMs Module

Introduction to LLMs

This module uncovers the core principles of Large Language Models (LLMs), zooming in on their foundational underpinnings. We provide a historical perspective, highlighting the emergence of Transformers and the difference between proprietary and open-source LLMs. Key attention is on recognizing and mitigating inherent issues like hallucinations and biases within these models.

The module is structured as follows, concisely describing each lesson.

  • What are Large Language Models? This lesson dives into the core principles of LLMs, highlighting the capabilities of notable models such as GPT-3 and GPT-4. We introduce the concepts of tokens, few-shot learning, emergent abilities, and the significance of scaling laws. As we explore the functions and outputs of these models, we emphasize the potential challenges of hallucinations and biases. Additionally, we also discuss the context size limitation in LLMs.
  • The Evolution of LLMs and Transformers: This lesson provides a chronological narrative of the progression in language modeling techniques. Starting with the foundational Bag of Words model from 1954, we navigate through significant milestones like TF-IDF, the groundbreaking Word2Vec with its semantic-rich word embeddings, and the sequence-processing capabilities of RNNs. Central to our exploration is the 2017 Transformer architecture, which set the stage for powerhouses like BERT, RoBERTa, and ELECTRA. This lesson only offers an overview without the deep technical intricacies, presenting a panoramic view of the model evolution in NLP.
  • A timeline of Large Language Models: This lesson steers towards a comprehensive overview of the advancements in the Large Language Models landscape, spotlighting models that marked distinct milestones such as GPT-3, PaLM, and Galactica. Recognizing the significant role of techniques like scaling and alignment tuning in the unprecedented capabilities exhibited by LLMs, we untangle the principles that steer these giants. From exploring the enigmatic emergent abilities to decoding the scaling laws, this lesson explores the phenomena driving the potency and performance of LLMs.
  • Emergent Abilities in LLMs: This lesson covers the unexpected skills that surface in Large Language Models as they grow beyond certain thresholds. As models expand, they exhibit unique capabilities influenced by factors like training compute. These emergent skills indicate performance leaps in LLMs as they scale, revealing unforeseen learning beyond what was initially anticipated.
  • Proprietary LLMs: This lesson introduces prominent proprietary Large Language Models such as GPT-4, ChatGPT, and Cohere, among others. We'll weigh the advantages and drawbacks of proprietary models against open-source counterparts. Practical demonstrations will guide students in executing API calls for select models.
  • Open-Source LLMs: This lesson offers insights into open-source Large Language Models, with a focus on LLaMA 2, Open Assistant, Dolly, and Falcon. We will explore their unique features, capabilities, and licensing details. Additionally, we'll discuss potential commercial uses and emphasize any restrictions within their licenses.
  • Understanding Hallucinations and Bias in LLMs: This lesson focuses on the challenges posed by hallucinations and biases in Large Language Models. We'll define hallucinations, provide examples, and discuss their impact on LLM use cases. We'll also explore methods to minimize these issues, such as retriever architectures. The session also covers the concept of bias, its origin in LLMs, and potential mitigation strategies, including approaches like constitutional AI.
  • Applications and Use-Cases of LLMs: This lesson highlights the leading applications and emerging trends of Large Language Models across industries. By referencing real-world news and examples, we illustrate the transformative impact of LLMs across sectors. While emphasizing the vast potential benefits, the module also underscores the importance of recognizing LLMs' limitations and potential challenges.

This section provided a comprehensive overview of Large Language Models, highlighting their evolution and significant milestones. Topics ranged from understanding emergent abilities in LLMs to discerning between proprietary and open-source models. Critical challenges like hallucinations and biases were also addressed.