Large language models (LLMs) have exploded in popularity over the last few years, revolutionizing natural language processing and AI. From chatbots to search engines to creative writing aids, LLMs are powering cutting-edge applications across industries. However, building useful LLM-based products requires specialized skills and knowledge. This guide will provide you with a comprehensive yet accessible overview of the key concepts, architectural patterns, and practical skills needed to effectively leverage the huge potential of LLMs.

What are Large Language Models and Why are They Important?

LLMs are a class of deep learning models that are pretrained on massive text corpora, allowing them to generate human-like text and understand natural language at an unprecedented level. Unlike traditional NLP models which rely on rules and annotations, LLMs like GPT-3 learn language skills in an unsupervised, self-supervised manner by predicting masked words in sentences. Their foundational nature allows them to be fine-tuned for a wide variety of downstream NLP tasks.

LLMs represent a paradigm shift in AI and have enabled applications like chatbots, search engines, and text generators which were previously out of reach. For instance, instead of relying on brittle hand-coded rules, chatbots can now have free-form conversations using LLMs like Anthropic’s Claude. The powerful capabilities of LLMs stem from three key innovations:

1. Scale of data: LLMs are trained on internet-scale corpora with billions of words, e.g. GPT-3 saw 45TB of text data. This provides broad linguistic coverage.
2. Model size: LLMs like GPT-3 have 175 billion parameters, allowing them to absorb all this data. Large model capacity is key to generalization.
3. Self-supervision: Rather than costly human labeling, LLMs are trained via self-supervised objectives which create “pseudo-labeled” data from raw text. This enables pretraining at scale.

Mastering the knowledge and skills to properly finetune and deploy LLMs will allow you to innovate new NLP solutions and products.

Key Concepts for Applying LLMs

While LLMs have incredible capabilities right out of the box, effectively utilizing them for downstream tasks requires understanding key concepts like prompting, embeddings, attention, and semantic retrieval.

Prompting Rather than inputs and outputs, LLMs are controlled via prompts – contextual instructions that frame a task. For instance, to summarize a text passage, we would provide examples like:

“Passage: Summary:”

The model then generates a summary in its output. Prompt engineering is crucial to steering LLMs effectively.

Embeddings

Word embeddings represent words as dense vectors encoding semantic meaning, allowing mathematical operations. LLMs utilize embeddings to understand word context.

Techniques like Word2Vec and BERT create embedding models which can be reused. Word2Vec pioneered the use of shallow neural networks to learn embeddings by predicting neighboring words. BERT produces deep contextual embeddings by masking words and predicting them based on bidirectional context.

Recent research has evolved embeddings to capture more semantic relationships. Google’s MUM model uses VATT transformer to produce entity-aware BERT embeddings. Anthropic’s Constitutional AI learns embeddings sensitive to social contexts. Multilingual models like mT5 produce cross-lingual embeddings by pretraining on over 100 languages simultaneously.

Attention

Attention layers allow LLMs to focus on relevant context when generating text. Multi-head self-attention is key to transformers analyzing word relations across long texts.

For example, a question answering model can learn to assign higher attention weights to input words relevant to finding the answer. Visual attention mechanisms focus on pertinent regions of an image.

Recent variants like sparse attention improve efficiency by reducing redundant attention computations. Models like GShard use mixture-of-experts attention for greater parameter efficiency. The Universal Transformer introduces depth-wise recurrence enabling modeling of longer term dependencies.

Understanding attention innovations provides insight into extending model capabilities.

Retrieval

Large vector databases called semantic indexes store embeddings for efficient similarity search over documents. Retrieval augments LLMs by allowing huge external context.

Powerful approximate nearest neighbor algorithms like HNSW, LSH and PQ enable fast semantic search even with billions of documents. For example, Anthropic’s Claude LLM uses HNSW for retrieval over a 500 million document index.

Hybrid retrieval combines dense embeddings and sparse keyword metadata for improved recall. Models like REALM directly optimize embeddings for retrieval objectives via dual encoders.

Recent work also explores cross-modal retrieval between text, images, and video using shared multimodal vector spaces. Mastering semantic retrieval unlocks new applications like multimedia search engines.

Architectural Patterns

While model training remains complex, applying pretrained LLMs is more accessible using tried and tested architectural patterns:

Text Generation Pipeline

Leverage LLMs for generative text applications via:

1. Prompt engineering to frame the task
2. LLM generation of raw text
3. Safety filters to catch issues
4. Post-processing for formatting

For instance, an essay writing aid would use a prompt defining the essay subject, generate text from the LLM, filter for sensicalness, then spellcheck the output.

Search and Retrieval

Build semantic search systems by:

1. Indexing a document corpus into a vector database for similarities
2. Accepting search queries and finding relevant hits via approximate nearest neighbor lookup
3. Feeding hits as context to a LLM to summarize and synthesize an answer

This leverages retrieval over documents at scale rather than relying solely on the LLM’s limited context.

Multi-Task Learning

Rather than training individual LLM specialists, multi-task models allow teaching one model multiple skills via:

1. Prompts framing each task
2. Joint fine-tuning across tasks
3. Adding classifiers on LLM encoder to make predictions

This improves overall model performance and reduces training costs.

Hybrid AI Systems

Combines the strengths of LLMs and more symbolic AI via:

1. LLMs handling open-ended language tasks
2. Rule-based logic providing constraints
3. Structured knowledge represented in a KG
4. LLM & structured data enriching each other in a “virtuous cycle”

This combines the flexibility of neural approaches with robustness of symbolic methods.

Key Skills for Applying LLMs

With these architectural patterns in mind, let’s now dig into practical skills for putting LLMs to work:

Prompt Engineering

Being able to effectively prompt LLMs makes or breaks applications. Key skills include:

• Framing tasks as natural language instructions and examples
• Controlling length, specificity, and voice of prompts
• Iteratively refining prompts based on model outputs
• Curating prompt collections around domains like customer support
• Studying principles of human-AI interaction

Prompting is part art and part science – expect to incrementally improve through experience.

Orchestration Frameworks

Streamline LLM application development using frameworks like LangChain, Cohere which make it easy to chain models into pipelines, integrate with data sources, and abstract away infrastructure.

LangChain offers a modular architecture for composing prompts, models, pre/post processors and data connectors into customizable workflows. Cohere provides a studio for automating LLM workflows with a GUI, REST API and Python SDK.

These frameworks utilize techniques like:

• Transformer sharding to split context across GPUs for long sequences
• Asynchronous model queries for high throughput
• Caching strategies like Least Recently Used to optimize memory usage
• Distributed tracing to monitor pipeline bottlenecks
• A/B testing frameworks to run comparative evaluations
• Model versioning and release management for experimentation
• Scaling onto cloud platforms like AWS SageMaker for elastic capacity

AutoML tools like Spell offer optimization of prompts, hparams and model architectures. AI Economist tunes pricing models for API consumption.

Evaluation & Monitoring

Evaluating LLM performance is crucial before deployment:

• Measure overall output quality via accuracy, fluency, coherence metrics
• Use benchmarks like GLUE, SuperGLUE comprising NLU/NLG datasets
• Enable human evaluation via frameworks like scale.com and LionBridge
• Monitor training dynamics with tools like Weights & Biases
• Analyze model behavior using techniques like LDA topic modeling
• Check for biases with libraries like FairLearn and WhatIfTools
• Continuously run unit tests against key prompts
• Track real-world model logs and drift using tools like WhyLabs
• Apply adversarial testing via libraries like TextAttack and Robustness Gym

Recent research improves efficiency of human evaluation via balanced pairing and subset selection algorithms. Models like DELPHI fight adversarial attacks using causality graphs and gradient masking. Responsible AI tooling remains an active area of innovation.

Multimodal Applications

Beyond text, LLMs open new frontiers in multimodal intelligence:

• Condition LLMs on images, video, speech and other modalities
• Unified multimodal transformer architectures
• Cross-modal retrieval across media types
• Generating captions, visual descriptions, and summaries
• Multimodal coherence and common sense

This extends LLMs beyond language to reasoning about the physical world.

In Summary

Large language models represent a new era in AI capabilities. Mastering their key concepts, architectural patterns, and hands-on skills will enable you to innovate new intelligent products and services. LLMs lower the barriers for creating capable natural language systems – with the right expertise, you can leverage these powerful models to solve real-world problems.