What is Retrieval-Augmented Generation (RAG)?

Summary

Marina Danilevsky, a Senior Research Scientist at IBM, explains Retrieval-Augmented Generation (RAG) for improving large language models (LLMs). She highlights the issues of outdated information and lack of sources in LLM responses. RAG addresses these by combining LLMs with a content store, ensuring answers are both current and sourced. This method retrieves relevant information before generating a response, reducing hallucinations and improving accuracy. Danilevsky emphasizes the importance of enhancing both the retriever and generative aspects of RAG for optimal performance.

Takeaways

• 🧠 Large language models (LLMs) generate text in response to prompts but can sometimes provide outdated or incorrect information.
• 🔍 The speaker, Marina Danilevsky, introduces Retrieval-Augmented Generation (RAG) as a framework to improve the accuracy and currency of LLMs.
• 🌌 An anecdote about the number of moons around planets illustrates the common issues of LLMs: lack of sourcing and outdated information.
• 📚 RAG incorporates a content store, which can be the internet or a closed collection of documents, to provide up-to-date and sourced information.
• 🔄 The RAG framework instructs the LLM to first retrieve relevant content from the content store before generating a response to a user's query.
• 📈 By using RAG, LLMs can provide evidence for their responses, addressing the challenge of outdated information without needing to retrain the model.
• 🔗 The framework helps LLMs to pay attention to primary source data, reducing the likelihood of hallucinating or leaking data.
• 🤔 RAG encourages the model to acknowledge when it does not know the answer, promoting honesty and avoiding misleading information.
• 🛠️ Continuous improvement of both the retriever and the generative model is necessary to ensure the LLM provides the best possible responses.
• 📈 The effectiveness of RAG depends on the quality of the retriever, which must provide high-quality grounding information for the LLM.
• 👍 The script concludes with an encouragement to like and subscribe to the channel for more insights on RAG and related topics.

Q & A

What is the main topic discussed in the video script?

The main topic discussed in the video script is Retrieval-Augmented Generation (RAG), a framework designed to improve the accuracy and currency of large language models (LLMs).

Who is Marina Danilevsky?

Marina Danilevsky is a Senior Research Scientist at IBM Research, and she introduces the concept of RAG in the script.

What is the 'Generation' part in the context of large language models?

The 'Generation' part refers to the ability of large language models (LLMs) to generate text in response to a user query, also known as a prompt.

What are the two main challenges with large language models as illustrated in the anecdote?

The two main challenges are the lack of a source to support the information provided and the outdatedness of the information, which can lead to incorrect responses.

What is the current number of moons orbiting Saturn according to the script?

According to the script, Saturn currently has 146 moons.

How does RAG address the issue of outdated information in LLMs?

RAG addresses the issue by incorporating a content store that can be updated with new information, ensuring that the LLM can access and generate responses based on the most current data.

What is the role of the content store in the RAG framework?

The content store in the RAG framework serves as a source of up-to-date and relevant information that the LLM can retrieve and use to inform its responses to user queries.

How does RAG help to reduce the likelihood of an LLM hallucinating or leaking data?

RAG reduces the likelihood by instructing the LLM to pay attention to primary source data before generating a response, which provides a more reliable grounding for the information provided.

What is the importance of the retriever in the RAG framework?

The retriever is crucial in the RAG framework as it provides the LLM with high-quality, relevant data that forms the basis for the model's responses, improving the accuracy and reliability of the information generated.

What is the potential downside if the retriever does not provide the LLM with high-quality information?

If the retriever does not provide high-quality information, the LLM may not be able to generate accurate responses, even to answerable user queries, which could lead to a lack of response or misinformation.

What does the script suggest about the future work on improving LLMs?

The script suggests that future work will focus on improving both the retriever to provide better quality data and the generative part of the LLM to ensure richer and more accurate responses to user queries.

Outlines

00:00

🤖 Introduction to Retrieval-Augmented Generation (RAG)

Marina Danilevsky, a Senior Research Scientist at IBM Research, introduces the concept of Retrieval-Augmented Generation (RAG), a framework designed to improve the accuracy and currency of large language models (LLMs). She explains the 'Generation' aspect of RAG, which involves LLMs generating text in response to user prompts. Danilevsky uses an anecdote about the number of moons in our solar system to illustrate common issues with LLMs, such as providing outdated or unsourced information. She contrasts this with the benefits of RAG, which involves first consulting a content store before generating a response, leading to more reliable and up-to-date answers.

05:00

🔍 Enhancing LLMs with RAG to Address Accuracy and Data Sourcing

In the second paragraph, Danilevsky delves deeper into how RAG addresses the challenges of outdated information and lack of data sourcing in LLMs. By instructing the model to consider primary source data before responding, RAG reduces the likelihood of the model hallucinating or leaking data. The framework also encourages the model to acknowledge when it lacks the knowledge to answer a question, promoting a more cautious and accurate approach. However, she notes that the effectiveness of RAG depends on the quality of the retriever and the generative model, emphasizing ongoing efforts at IBM to refine both components for optimal performance.

Keywords

💡Large Language Models (LLMs)

Large Language Models (LLMs) refer to artificial intelligence systems that are trained on vast amounts of text data and can generate human-like responses to various queries. In the video, LLMs are highlighted for their ability to generate text but also for their potential inaccuracies and outdated information. The script discusses how LLMs can be improved by incorporating a retrieval-augmented generation framework to ensure more accurate and up-to-date responses.

💡Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is a framework that enhances the capabilities of large language models by incorporating external information retrieval before generating a response. This approach is central to the video's theme, as it addresses the limitations of LLMs and demonstrates how RAG can provide more accurate and current information, as illustrated by the anecdote about the number of moons orbiting Jupiter and Saturn.

💡Generation

In the context of the video, 'generation' refers to the process by which LLMs produce text in response to user queries, known as prompts. The script emphasizes the importance of this process and how it can be improved with RAG to ensure that the generated responses are not only based on the model's pre-existing knowledge but also on up-to-date, retrieved information.

💡User Query

A user query, as mentioned in the script, is the question or prompt input by a user to elicit a response from an LLM. The video discusses how the quality of the response can be enhanced by first retrieving relevant information before generating a reply to the user query, which is a key aspect of the RAG framework.

💡Desirable Behavior

Desirable behavior in the context of the video pertains to the accurate and up-to-date responses provided by LLMs. The script contrasts this with undesirable behavior, such as providing outdated or incorrect information, and explains how RAG can help LLMs exhibit more desirable behavior by incorporating current data.

💡Anecdote

An anecdote is a short, interesting story used to illustrate a point. In the video, the speaker uses an anecdote about her children asking about the planet with the most moons to highlight the limitations of relying on memory and the importance of checking current, reliable sources, which parallels the function of RAG in LLMs.

💡Source

In the video, 'source' refers to the origin of information that an LLM uses to generate a response. The script points out the importance of sourcing information from reputable and current sources to avoid providing outdated or incorrect data, which is a key benefit of the RAG framework.

💡Out of Date

The term 'out of date' in the script refers to information that is no longer current or accurate. The video uses this concept to discuss the limitations of LLMs, which may provide responses based on outdated knowledge, and how RAG can help by retrieving the most recent information from a content store.

💡Content Store

A content store, as mentioned in the script, is a repository of information that can be either open, like the internet, or closed, like a specific collection of documents. The video explains that in the RAG framework, the LLM retrieves relevant information from the content store to provide more accurate and up-to-date responses.

💡Evidence

In the context of the video, 'evidence' refers to the supporting information or data that backs up the response generated by an LLM. The script explains that with RAG, LLMs can provide evidence for their responses, making them more reliable and less prone to 'hallucination' or providing fabricated answers.

💡Hallucination

In the video, 'hallucination' is used to describe the phenomenon where an LLM generates responses based on its training data rather than current or factual information, potentially leading to inaccuracies. The script discusses how RAG can reduce this issue by instructing the LLM to retrieve and consider up-to-date information before generating a response.

💡Primary Source Data

Primary source data, as discussed in the script, refers to original, firsthand information that is used as the basis for an LLM's response in the RAG framework. The video emphasizes the importance of using primary source data to ensure that the responses are accurate and grounded in reality.

💡I Don't Know

The phrase 'I don't know' in the script represents a positive behavior for LLMs, indicating that they should acknowledge when they cannot provide a reliable answer based on the available data. The video suggests that RAG encourages this behavior, which can prevent misleading users with fabricated or inaccurate information.

Highlights

Marina Danilevsky introduces Retrieval-Augmented Generation (RAG), a framework to improve the accuracy and currency of large language models (LLMs).

LLMs can confidently generate text in response to prompts but may have outdated or unverified information.

An anecdote illustrates the problem of relying on outdated knowledge without sourcing information, like mistakenly identifying Jupiter as the planet with the most moons.

The importance of verifying information with reputable sources, such as NASA, to provide current and accurate answers, like Saturn having 146 moons.

RAG enhances LLMs by incorporating a retrieval component that accesses a content store for up-to-date, relevant information.

The retrieval-augmented approach allows LLMs to ground their responses in primary source data, reducing the likelihood of misinformation.

RAG framework instructs the generative model to first retrieve relevant content, combine it with the user's question, and then generate an answer.

The prompt in RAG consists of three parts: the instruction, the retrieved content, and the user's question.

RAG addresses the challenge of outdated information by allowing LLMs to access the most current data without needing retraining.

LLMs using RAG are less likely to hallucinate or leak data, as they are instructed to consider primary source data before responding.

RAG enables LLMs to know when to say 'I don't know,' avoiding the generation of misleading information when the data store cannot provide a reliable answer.

The potential downside of RAG is that if the retriever does not provide high-quality grounding information, some answerable queries may go unanswered.

IBM researchers, including Marina Danilevsky, are working on improving both the retriever and the generative components of LLMs to enhance the quality of responses.

RAG represents an innovative method to make LLMs more accurate, up-to-date, and capable of providing evidence for their responses.

The framework has practical applications in improving the reliability and utility of LLMs in various domains.

The importance of ongoing research and development in both the retrieval and generation aspects of LLMs to ensure the best user experience.

Transcripts

Large language models. They are everywhere.

They get some things amazingly right

and other things very interestingly wrong.

My name is Marina Danilevsky.

I am a Senior Research Scientist here at IBM Research.

And I want to tell you about a framework to help large language models

be more accurate and more up to date:

Retrieval-Augmented Generation, or RAG.

Let's just talk about the "Generation" part for a minute.

So forget the "Retrieval-Augmented".

So the generation, this refers to large language models, or LLMs,

that generate text in response to a user query, referred to as a prompt.

These models can have some undesirable behavior.

I want to tell you an anecdote to illustrate this.

So my kids, they recently asked me this question:

"In our solar system, what planet has the most moons?"

And my response was, “Oh, that's really great that you're asking this question. I loved space when I was your age.”

Of course, that was like 30 years ago.

But I know this! I read an article

and the article said that it was Jupiter and 88 moons. So that's the answer.

Now, actually, there's a couple of things wrong with my answer.

First of all, I have no source to support what I'm saying.

So even though I confidently said “I read an article, I know the answer!”, I'm not sourcing it.

I'm giving the answer off the top of my head.

And also, I actually haven't kept up with this for awhile, and my answer is out of date.

So we have two problems here. One is no source. And the second problem is that I am out of date.  

And these, in fact, are two behaviors that are often observed as problematic

when interacting with large language models. They’re LLM challenges.

Now, what would have happened if I'd taken a beat and first gone

and looked up the answer on a reputable source like NASA?

Well, then I would have been able to say, “Ah, okay! So the answer is Saturn with 146 moons.”

And in fact, this keeps changing because scientists keep on discovering more and more moons.

So I have now grounded my answer in something more  believable.

I have not hallucinated or made up an answer.

Oh, by the way, I didn't leak personal information about how long ago it's been since I was obsessed with space.

All right, so what does this have to do with large language models?

Well, how would a large language model have answered this question?

So let's say that I have a user asking this question about moons.

A large language model would confidently say,

OK, I have been trained and from what I know in my parameters during my training, the answer is Jupiter.

The answer is wrong. But, you know, we don't know.

The large language model is very confident in what it answered.

Now, what happens when you add this retrieval augmented part here?

What does that mean?

That means that now, instead of just relying on what the LLM knows,

we are adding a content store.

This could be open like the internet.

This can be closed like some collection of documents, collection of policies, whatever.

The point, though, now is that the LLM first goes and talks

to the content store and says, “Hey, can you retrieve for me

information that is relevant to what the user's query was?”

And now, with this retrieval-augmented answer, it's not Jupiter anymore.

We know that it is Saturn. What does this look like?

Well, first user prompts the LLM with their question.

They say, this is what my question was.

And originally, if we're just talking to a generative model,

the generative model says, “Oh, okay, I know the response. Here it is. Here's my response.”  

But now in the RAG framework,

the generative model actually has an instruction that says, "No, no, no."

"First, go and retrieve relevant content."

"Combine that with the user's question and only then generate the answer."

So the prompt now has three parts:

the instruction to pay attention to, the retrieved content, together with the user's question.

Now give a response. And in fact, now you can give evidence for why your response was what it was.  

So now hopefully you can see, how does RAG help the two LLM challenges that I had mentioned before?  

So first of all, I'll start with the out of date part.

Now, instead of having to retrain your model, if new information comes up, like,

hey, we found some more moons-- now to Jupiter again, maybe it'll be Saturn again in the future.

All you have to do is you augment your data store with new information, update information.

So now the next time that a user comes and asks the question, we're ready.

We just go ahead and retrieve the most up to date information.

The second problem, source.

Well, the large language model is now being instructed to pay attention

to primary source data before giving its response.

And in fact, now being able to give evidence.

This makes it less likely to hallucinate or to leak data

because it is less likely to rely only on information that it learned during training.

It also allows us to get the model to have a behavior that can be very positive,

which is knowing when to say, “I don't know.”

If the user's question cannot be reliably answered based on your data store,

the model should say, "I don't know," instead of making up something that is believable and may mislead the user.

This can have a negative effect as well though, because if the retriever is not sufficiently good

to give the large language model the best, most high-quality grounding information,

then maybe the user's query that is answerable doesn't get an answer.

So this is actually why lots of folks, including many of us here at IBM,

are working the problem on both sides.

We are both working to improve the retriever

to give the large language model the best quality data on which to ground its response,

and also the generative part so that the LLM can give the richest, best response finally to the user

when it generates the answer.

Thank you for learning more about RAG and like and subscribe to the channel.

Thank you.