Why Longer Context Isn't Enough

Summary

The video script discusses a startup's approach to developing coding assistants that learn and adapt in real-time with the user. Unlike existing tools that struggle with new libraries or research papers, the startup's models are continually trained in production to handle new problems as they arise. The speaker addresses a common question about why they don't use Retrieval-Augmented Generation (RAG), a method where models are provided with relevant context before a task. They explain that while RAG is powerful, it has limitations, such as the potential absence of necessary context and the model's learning scope being confined to its pre-training data. The speaker argues that for solving cutting-edge problems and innovating beyond current human knowledge, continual learning is crucial, not just in-context learning. They emphasize the importance of training models even in production, despite the cost, to unlock the full potential of the models. The video ends with an invitation for collaboration on a related research project.

Takeaways

• 🤖 The startup is developing coding assistants that learn and adapt in real-time with the user, unlike traditional tools that struggle with new or niche problems.
• 📚 In-context learning involves providing relevant context to a model before a task, allowing it to extract information from that context to solve the task.
• 🔍 Retrieval Augmented Generation (RAG) is a method where the model retrieves and uses relevant context to generate useful outputs, which is a popular approach in AI.
• 🚧 A common question when pitching the startup is why not use RAG to solve the problem, considering its efficiency and the advancements in long context length for models.
• 💡 The speaker argues that in-context learning and RAG have limitations, such as not always finding the needed context or the existence of problems without existing references.
• 🧐 The scope of learning for a model is restricted by its pre-training data, which means it may not be effective for tasks outside its training domain.
• 🛠️ The speaker's startup opts for continual training of models in production to overcome the limitations of pre-trained models and to innovate beyond current human knowledge.
• 💰 Continually training models is more expensive and slower compared to in-context learning, but it's deemed necessary for the startup's ambitious goals.
• 🔥 The speaker is currently working on a research project related to these challenges in their spare time and is open to collaboration with others who have the relevant skills.
• 📧 Interested individuals with programming and machine learning skills are encouraged to reach out for potential collaboration via the email on the speaker's channel.
• 📺 The video concludes with a call to action for viewers to subscribe for more content on the topic.

Q & A

What is the main challenge that the startup is addressing with their coding assistants?

The startup is addressing the challenge of adapting coding assistants to new libraries or niche research papers that the models have never been trained on before. Existing tools often struggle with these novel situations.

What is the concept of 'in-context learning'?

In-context learning is a method where a model is provided with relevant context before being prompted with a task. This allows the model to extract information from the given context to solve the main task at hand, without the need for additional training or back propagation.

What is 'retrieval augmented generation' (RAG)?

Retrieval augmented generation (RAG) is a technique where relevant context is retrieved and used to generate something useful. It is popular for tasks like programming where documentation or code snippets can be provided to the model to enhance its performance.

Why does the speaker argue that RAG and in-context learning are not sufficient for their startup's goals?

The speaker argues that RAG and in-context learning are not sufficient because they may not always have the necessary context available, especially for new or niche problems. Additionally, the scope of what a model can learn is limited by its pre-training data.

What are the two critical shortcomings of the RAG approach mentioned in the script?

The two critical shortcomings are: 1) The lack of available context for new or niche problems, and 2) The limitation of a model's learning scope by its pre-training data, which restricts the types of patterns it can recognize and the things it can learn in context.

What is the speaker's stance on the use of in-context learning in their startup?

The speaker acknowledges that in-context learning is a powerful tool but asserts that it alone is not enough to solve the complex problems they aim to address. They advocate for continual learning even in a production environment.

Why is continual training of models in production considered important by the speaker?

Continual training is important because it allows the models to adapt to new problems in real-time, expanding their potential beyond the limitations of their pre-training data, and enabling them to solve more complex and novel problems.

What is the speaker's current project related to the discussed topic?

The speaker is working on a research project related to the discussed topic in their spare time, aiming to enhance the capabilities of coding assistants beyond the limitations of in-context learning.

How does the speaker propose to overcome the limitations of in-context learning?

The speaker proposes continual learning, where models are trained on new topics in real-time as they arise, allowing them to adapt and learn from new data continuously.

What is the speaker's call to action for individuals interested in collaborating on the project?

The speaker invites individuals with programming skills, familiarity with machine learning, and an interest in the subject matter to reach out to them via the email link on their channel for potential collaboration.

What is the significance of long context length in the recent advancements of large language models (LLMs)?

The significance of long context length is that it allows models to process and understand more information, which can lead to better accuracy and performance on complex tasks. This advancement makes techniques like RAG and in-context learning more viable.

Why might a model trained primarily on code struggle with generating high-quality poetry?

A model trained primarily on code may struggle with generating high-quality poetry because its understanding and ability to recognize what makes examples high quality is tied to the subject matter it was trained on. It may not have the necessary context or 'skills' to evaluate and create high-quality poetry.