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.

Outlines

00:00

🤖 Continually Training Coding Assistants vs. In-Context Learning

The speaker discusses their startup's approach to developing coding assistants that learn and adapt in real-time alongside the user. They address the challenge of handling new libraries or research papers that the model hasn't encountered before, contrasting their method with in-context learning and retrieval augmented generation (RAG). The speaker explains that while in-context learning is powerful, it has limitations, such as the potential lack of relevant context or the model's pre-training data constraining the scope of learning. They argue that continual training, despite its cost and slower pace, is necessary to push the boundaries of what the model can achieve.

05:01

🧠 The Limitations of In-Context Learning for Advanced Problem Solving

The speaker elaborates on why relying solely on in-context learning is insufficient for the complex problems they aim to solve with their coding assistants. They highlight two critical shortcomings: the unavailability of necessary context for some niche or new problems and the limitation of a model's learning scope by its pre-training data. The speaker uses the example of a model trained primarily on code and documentation to illustrate how in-context learning skills can be tied to specific domains. They emphasize the importance of continual learning for models that aim to innovate and surpass human capabilities, stating that without it, the model's potential is limited. The speaker also invites collaboration from those with programming skills and machine learning knowledge interested in contributing to their research project.

Keywords

💡Coding Assistants

Coding Assistants are AI tools designed to aid programmers by providing code suggestions, debugging help, and other forms of assistance. In the context of the video, they are being continually trained in production to adapt to new problems, such as working with unfamiliar libraries or implementing novel research concepts.

💡In-Context Learning

In-Context Learning is a method where an AI model is provided with relevant context before being prompted with a task. This approach allows the model to extract information from the given context to solve the task at hand. In the video, it is discussed as a potential alternative to the continual training of models, but the speaker argues that it has limitations.

💡Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a technique that involves retrieving relevant context and using it to generate useful outputs. It is a popular approach in AI for tasks like programming, where documentation for a library can be used to assist the model in generating code. The video discusses RAG as a common suggestion for improving AI performance without continual training.

💡Long Context Length

Long Context Length refers to the ability of AI models to process and understand large amounts of contextual information, which has significantly improved in recent years. The video highlights how this advancement has made techniques like RAG and in-context learning more viable, as they can now handle up to 10 million tokens.

💡Continual Training

Continual Training involves the ongoing process of training AI models with new data to improve their performance and adaptability. The video's speaker argues for this approach over in-context learning, stating that it allows models to evolve and solve more complex and novel problems beyond the scope of their initial training.

💡Pre-Training Data

Pre-Training Data is the dataset used to initially train an AI model before it is fine-tuned for specific tasks. The video discusses how the scope of what a model can learn through in-context learning is limited by the nature of this pre-training data, which influences the types of patterns it can recognize and the skills it can acquire.

💡 Niche Research

Niche Research refers to specialized areas of study that are not widely covered or understood. The video mentions that when working with niche research, existing tools may not be sufficient, and the continual training of models is necessary to adapt to the unique challenges these areas present.

💡Domain-Specific Skills

Domain-Specific Skills are abilities that are tailored to a particular subject area or field of study. The video explains that in-context learning can involve a group of skills that may be tied to a specific domain, such as a model trained on code being adept at generating high-quality code but not necessarily poetry.

💡Foundation Models

Foundation Models are pre-trained AI models that are designed to be versatile and capable of solving a wide range of tasks. The video contrasts these models with the need for specialized models that can go beyond generic tasks and invent new solutions, emphasizing the importance of continual learning for the latter.

💡Continual Learning

Continual Learning is the process of allowing AI models to learn and adapt over time, even after their initial training. The video argues that this is crucial for models to solve the most complex and novel problems, as it enables them to break through the limitations imposed by their pre-training data.

💡Collaboration

Collaboration is the act of working together, particularly in the context of the video, it refers to the speaker's invitation for others with programming and machine learning skills to join in a research project. This highlights the interdisciplinary nature of the work and the need for collective effort to tackle complex AI challenges.

Highlights

The startup is developing coding assistants that learn and adapt in real-time to new problems as they arise.

Models are continually trained in production to handle new libraries or research papers not seen before.

Existing tools like GitHub Copilot struggle with new or niche coding tasks.

The concept of in-context learning is introduced, where the model is provided with relevant context before a task.

In-context learning allows models to extract information from given context to solve tasks without additional training.

Retrieval augmented generation (RAG) is a popular approach combining retrieval of relevant context with generation of outputs.

The speaker经常被问到为什么不使用RAG来解决新问题。

Long context length in large language models (LLMs) has improved significantly in recent years, making RAG more viable.

The continual training approach is more expensive and slower compared to in-context learning.

Two critical shortcomings of RAG are identified: the unavailability of necessary context and the limitation of pre-training data scope.

In niche or hard problems, the required information may not exist, making RAG insufficient.

The model's ability to learn in context is constrained by what it was pre-trained on.

Foundation models like Chachi PT and Claude are trained on a wide variety of data for generic tasks.

For models to solve the most complex problems and innovate, they need to go beyond their pre-training data.

Continual learning is essential for a model to reach its full potential and solve unprecedented problems.

In-context learning is a powerful tool but not sufficient on its own for the most challenging problems.

The speaker is working on a research project related to this topic in their spare time and invites collaboration.

An invitation to subscribe for more content and a thank you for watching concludes the video.

Transcripts

for nearly the past year I've been

working on a startup where we're

deploying coding assistants that learn

together with the user we're actually

continually training these models in

production so they can adapt to any new

problem that may arise for example if a

user starts working with a new library

that the model's never been trained on

or perhaps they're trying to implement

some Niche research paper that the

model's never seen before existing tools

like ithub co-pilot tend to really fall

flat so to solve this we're continually

training these models on these new

topics in real time as they come up but

whenever we pitch this idea we always

get the exact same question which is why

don't you just gather the relevant data

pass it into your model's context and

solve this problem with in context

learning before I give you my response

I'll briefly explain what in context

learning is it's basically just what it

sounds like before you prompt a model

with some task the idea is that you

first give it relevant context so in the

case of programming this could be

something like documentation for a

library that you're using you would pass

the documentation into the model and

then because these models are trained

with a long context window and with this

contextual information along with

relevant tasks they tend to learn to

extract information from that given

context which they can then use to solve

the main task at hand and this is a

notable thing because it means that you

could do something like pass the model

documentation for a new library that

it's never seen before and it could

potentially still work without any

additional training or back propagation

this whole approach of retrieving

relevant context and then using it to

generate something useful is popularly

known as retrieval augmented generation

or rag for short and we really do get

this question pretty much every time we

give a technical pitch it's always why

don't you use Rag and this is honestly a

very reasonable thing to ask given the

recent history of llms just a few years

ago the maximum context length you could

get was something like 2 to 4,000 tokens

now you can get 10 million tokens at a

fraction of the cost with significantly

better accuracy that is crazy and if we

consider the fact that Lins are probably

going to keep getting better it makes a

lot of sense to try and solve these

problems with things that rely on this

long context length like Rag and in

context learning and it's not to mention

that our approach of continually

training models is considerably slower

and more expensive so obviously this

raises the question why would anyone

ever go with our approach of training

models in production if you want a

little challenge actually pause the

video take a second and really think

about this heck leave a comment if you

think you know where I'm going with this

also you might as well subscribe while

you're at it okay so hopefully you've

taken a second to think about it if you

want uh so why do I say that in context

learning and rag are not enough in the

title of this video or probably

something like in context learning isn't

enough well there are two critical

shortcomings of a rag approach when it

comes to doing what we're doing

the first is that you won't necessarily

always be able to find the context you

need and sometimes the right context

won't even exist if for example you've

ever worked with new Niche libraries or

God forbid internal tooling at any

software company you'll know what I'm

talking about sometimes the

documentation just doesn't exist which

you know it sucks uh and in this case

maybe you could try something like

retrieving relevant code Snippets and

using that instead and maybe maybe just

maybe that would kind of work but the

point is is that as the problems you

solving approach the boundary of what

humans have solved before which is

basically what research is you'll

eventually get to the point where there

are no references that tell you how to

do what you want to do and rag can be a

great tool for many problems but it

alone does not enable models to solve

these sorts of Niche or very hard

problems that we want our models to be

able to solve because in these cases the

information we often need just doesn't

exist in the first place so that's the

first reason we're not doing Rag and the

second critical shortcoming of in

context learning specifically is that

the scope of what a model can learn in

context is limited by the model's

pre-training data let me explain what I

mean by this with an example to

clarify if we were to train an llm on

primarily code and documentation we

would expect it to know about things

like loops and conditionals and we would

expect it to be good at programming but

you know not so good at something like

writing poetry because well it wasn't

trained on poetry in the same way that

understanding loops and conditionals are

skills that a model can learn and

context learning is just another skill

that a model can learn though rather

than one skill it's more like a group of

skills that includes things like

learning via example learning to use

documentation learning to infer via

induction and so on and depending on the

exact topics model and learning

algorithm these in context learning

skills may also be tied to a specific

domain for for example a model trained

on code may be great at using examples

of existing highquality code Snippets to

generate new samples of high quality

code because it understands what makes

the examples high quality but if given

examples of high quality poetry it may

fail to generate new high quality poetry

because it doesn't necessarily

understand what makes the initial

examples high quality I'm giving this

example to illustrate the point that in

context learning actually consists of

many different skills that are not

always independent of the topic of the

context so in this prior example that's

to say the ability to learn via example

was tied to the subject matter so now

getting back to my point if you want to

use a large Foundation model to solve

some generic task this point is of no

consequence to you models like Chachi PT

and Claude are intentionally trained on

a massive variety of data so that they

will work on a massive variety of

generic problems however if you want a

model to solve the most interesting

problems invent genuinely new Solutions

and surpass the limits of human

knowledge and ability then a model with

frozen weights won't get you there even

if it can learn in context the types of

patterns it can recognize and the types

of things it can learn in context will

be limited by its pre-training data this

is why continual learning is important

and it's why we're training models even

in production at my startup even if it

is expensive it's because without

continual learning you are limiting the

potential of what your model can learn

note that I'm not saying we shouldn't

use in context learning in context

learning is in fact a very powerful tool

rather I'm saying in context learning

alone is not enough to solve the types

of problems that I want to solve this is

a problem I care a lot about and I'm

currently working on a research project

related to this in my spare time but

it's not something I have time to do

alone if you have programming skills

some familiarity with ml in the subject

matter and would be interested in

collaborating uh do reach out to me via

the email link on my Channel but that's

all for now subscribe if you want to see

more of this and thank you so much for

watching