Why Longer Context Isn't Enough
Summary
TLDRThe 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
🤖 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.
🧠 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.
Mindmap
Keywords
💡Coding Assistants
💡In-Context Learning
💡Retrieval-Augmented Generation (RAG)
💡Long Context Length
💡Continual Training
💡Pre-Training Data
💡 Niche Research
💡Domain-Specific Skills
💡Foundation Models
💡Continual Learning
💡Collaboration
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
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