Beyond the Hype: A Realistic Look at Large Language Models • Jodie Burchell • GOTO 2024

Summary

Jody Burell's talk at JetBrains dives into the world of Large Language Models (LLMs), debunking myths and hype around AI's potential for general intelligence. With a background in data science and NLP, Burell provides historical context, explains the evolution of neural networks, and critiques the sensationalism around models like GPT. The talk explores practical applications of LLMs in NLP tasks, the concept of generalization in AI, and introduces Retrieval-Augmented Generation (RAG) for enhancing question-answering capabilities. Burell emphasizes the importance of selecting the right model and use case, and the need for careful measurement and performance tuning in deploying LLMs.

Takeaways

• 😀 Jody Burell, a developer advocate at JetBrains, discusses the evolution and capabilities of Large Language Models (LLMs), emphasizing their roots in NLP and data science.
• 🧠 Burell highlights the AI hype cycle and the sensational claims about LLMs, such as showing signs of artificial general intelligence (AGI), replacing white-collar jobs, and even leading to an AI apocalypse.
• 📈 The talk outlines the historical development of neural nets, the advent of CUDA for efficient matrix multiplication, and the creation of large datasets like Common Crawl, which enabled the training of more complex language models.
• 🔄 Burell explains the limitations of early models like LSTMs and the breakthrough that was the introduction of Transformer models, which allowed for the creation of much larger and more contextually aware models.
• 🌟 The GPT (Generative Pre-trained Transformer) models are presented as a significant leap in text generation and understanding, with each new version improving upon the last, culminating in models like GPT-4 with an estimated trillion parameters.
• 🕵️‍♂️ The speaker challenges the idea that LLMs demonstrate AGI, using the example of Deep Blue's chess victory over Garry Kasparov to illustrate the difference between skill-based assessments and true intelligence.
• 🔍 Burell introduces the concept of generalization in AI, with levels ranging from no generalization to universality, suggesting that current LLMs are far from achieving human-like intelligence or universal problem-solving.
• 🛠️ The talk demonstrates practical applications of LLMs, such as question answering, fine-tuning for specific domains, and Retrieval-Augmented Generation (RAG), which combines an LLM with external data retrieval for more accurate responses.
• 🔧 Burell provides a live demo using Lang chain, an open-source package, to create a simple RAG pipeline for question answering, showcasing how LLMs can be extended for specific tasks like searching through documentation.
• 🚧 The speaker concludes with a cautionary note on the complexities and potential pitfalls of deploying LLMs, emphasizing the importance of selecting the right model, tuning the application, and measuring performance for specific use cases.
• 💡 Lastly, Burell suggests that while LLMs are powerful, they are not a panacea and should be approached with the same rigor and methodology as any other software or machine learning tool.

Q & A

What is Jody Burell's current role at JetBrains?

Jody Burell is currently working as a Developer Advocate at JetBrains.

How long has Jody Burell been working as a data scientist?

Jody Burell has been working as a data scientist for almost 10 years.

What was the initial aim of earlier models in the field of natural language processing?

The initial aim of earlier models in natural language processing was to automate tasks that require huge amounts of manual labor, such as text classification or summarization, not text generation.

What technological breakthrough allowed the training of large neural networks to become more feasible?

The development of CUDA, which transformed GPUs into all-purpose matrix multiplication machines, made the training of large neural networks more feasible.

What is the significance of the development of the common crawl dataset in the context of large language models?

The development of the common crawl dataset provided researchers with sufficient text data to start training more complex language models.

What type of neural network was developed in 2007 that helped capture relationships between words in context?

Long Short-Term Memory networks (LSTMs) were developed in 2007 to capture relationships between words in context.

How do Transformer models differ from LSTMs in terms of processing text?

Transformer models differ from LSTMs by avoiding sequential processing, allowing models to get really large and learn rich internal representations of how language works.

What is the foundational architecture of most large language models released in the last three years?

Most large language models released in the last three years have a GPT-based architecture.

What is the main issue with using skill-based assessments to determine the intelligence of AI systems?

Skill-based assessments can be misleading as they focus on how AI models perform in specific tasks, ignoring how intelligence is actually defined and measured in humans.

How does François Chollet define artificial general intelligence?

François Chollet defines artificial general intelligence as an artificial system's ability to solve a task by using knowledge encoded in a skill program relevant to that task, with the skill program being generated and refined by a humanlike intelligent system.

What is the main purpose of the Rag (Retrieval-Augmented Generation) pipeline in question answering?

The main purpose of the Rag pipeline is to pull in additional context relevant to the input prompt from an external source, which is then incorporated into the prompt to help the language model answer the question more accurately.

What are some of the challenges associated with deploying and working with Retrieval-Augmented Generation (RAG) applications?

Challenges with RAG include tuning various parameters like the number of chunks returned per query, choosing the right model for the task, and ensuring the model has been trained on the relevant data to avoid poor performance and hallucinations.

How can one assess whether a language model is suitable for a specific task?

One can assess a language model's suitability for a specific task by looking at domain-specific benchmarks or creating a custom benchmarking dataset, and considering the model's training data and performance on similar tasks.

What is the importance of measuring performance when working with large language models?

Measuring performance is crucial to ensure that the model is appropriately tuned for the task, to avoid providing poor quality answers, and to confirm that the model is not hallucinating information.