What is Retrieval-Augmented Generation (RAG)?

00:06:35
https://www.youtube.com/watch?v=T-D1OfcDW1M

Ringkasan

TLDRIn her talk, Marina Danilevsky from IBM Research discusses the limitations of large language models (LLMs) and introduces the Retrieval-Augmented Generation (RAG) framework as a solution to improve their accuracy and ensure their responses are up to date. LLMs, while capable of generating text in answer to prompts, often provide outdated or unsourced information. Danilevsky illustrates this with a personal anecdote about erroneous planet moon counts. The RAG framework addresses these challenges by integrating a retrieval step, where the model accesses a content store—be it open or closed—to fetch relevant and accurate data. This process allows the LLM to produce responses grounded in factual, current information, helping to reduce misinformation or "hallucination" and teaching the model when to acknowledge "I don't know" if the answer isn't found in the data source. While RAG enhances LLMs' reliability, it requires effective retrieval systems to function optimally and avoid incomplete guidance.

Takeaways

  • 💡 Introducing Retrieval-Augmented Generation (RAG) can enhance the accuracy of LLMs by anchoring their responses in updated and reliable information.
  • 🚀 LLMs may struggle with outdated or unsourced data, leading to errors in responses.
  • 🔍 The retrieval step in RAG leverages both open and closed content sources for enriched responses.
  • 🔄 RAG allows LLMs to admit "I don't know," reducing the risk of misleading responses.
  • 🌟 Incorporating RAG helps identify when retriever quality affects LLM response accuracy.
  • 🧠 Grounding LLM outputs in primary data can diminish hallucination risks, enhancing trust in AI responses.
  • 📚 The RAG framework taps into content stores, making retrained data less crucial for up-to-date information.
  • 🔗 Combining retrieval with generation helps LLMs connect queries to accurate responses.
  • 🎯 Optimizing RAG entails refining both retrieval processes and generative models.
  • 🔢 By citing evidence, RAG-augmented responses provide more reliable information to users.

Garis waktu

  • 00:00:00 - 00:06:35

    Large language models (LLMs) exhibit both impressive successes and notable faults. Challenges associated with LLMs include generating unsupported or outdated information, akin to providing an answer without checking current sources. Retrieval-Augmented Generation (RAG) is a framework designed to address these issues by integrating real-time data retrieval into the generative process.

Peta Pikiran

Video Tanya Jawab

  • What is RAG in the context of this presentation?

    RAG stands for Retrieval-Augmented Generation, a framework to improve the accuracy and recency of large language models.

  • What problem does RAG aim to solve for large language models?

    RAG aims to solve the issues of outdated information and lack of sourcing in the responses of large language models.

  • How does RAG improve the accuracy of LLMs?

    By incorporating a retrieval process to gather up-to-date information from reputable sources before generating a response.

  • What example was used to illustrate LLM challenges?

    Marina Danilevsky used the question about which planet has the most moons in the solar system, showing how outdated and unsourced information can lead to errors.

  • How does RAG help avoid the hallucination problem in LLMs?

    By ensuring the model retrieves and uses primary source data before generating a response.

  • What is the benefit of LLMs admitting 'I don't know' according to Marina Danilevsky?

    It prevents the model from generating false or misleading information.

  • Can RAG be integrated with both open and closed information sources?

    Yes, RAG can work with both open sources (like the internet) and closed ones (like private collections).

  • How does the retrieval process in RAG work?

    The language model queries a content store for relevant information, combines it with the user's question, and then generates an informed and accurate response.

  • Who is Marina Danilevsky?

    Marina Danilevsky is a Senior Research Scientist at IBM Research.

  • What is a potential downside of using RAG?

    If the retrieval process is not effective, it might fail to provide the language model with the best grounding information, potentially leading to incomplete answers.

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Teks
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Gulir Otomatis:
  • 00:00:00
    Large language models. They are everywhere.
  • 00:00:02
    They get some things amazingly right
  • 00:00:05
    and other things very interestingly wrong.
  • 00:00:07
    My name is Marina Danilevsky.
  • 00:00:09
    I am a Senior Research Scientist here at IBM Research.
  • 00:00:12
    And I want to tell you about a framework to help large language models
  • 00:00:16
    be more accurate and more up to date:
  • 00:00:18
    Retrieval-Augmented Generation, or RAG.
  • 00:00:22
    Let's just talk about the "Generation" part for a minute.
  • 00:00:24
    So forget the "Retrieval-Augmented".
  • 00:00:26
    So the generation, this refers to large language models, or LLMs,
  • 00:00:31
    that generate text in response to a user query, referred to as a prompt.
  • 00:00:36
    These models can have some undesirable behavior.
  • 00:00:38
    I want to tell you an anecdote to illustrate this.
  • 00:00:41
    So my kids, they recently asked me this question:
  • 00:00:44
    "In our solar system, what planet has the most moons?"
  • 00:00:48
    And my response was, “Oh, that's really great that you're asking this question. I loved space when I was your age.”
  • 00:00:55
    Of course, that was like 30 years ago.
  • 00:00:58
    But I know this! I read an article
  • 00:01:00
    and the article said that it was Jupiter and 88 moons. So that's the answer.
  • 00:01:06
    Now, actually, there's a couple of things wrong with my answer.
  • 00:01:10
    First of all, I have no source to support what I'm saying.
  • 00:01:14
    So even though I confidently said “I read an article, I know the answer!”, I'm not sourcing it.
  • 00:01:18
    I'm giving the answer off the top of my head.
  • 00:01:20
    And also, I actually haven't kept up with this for awhile, and my answer is out of date.
  • 00:01:26
    So we have two problems here. One is no source. And the second problem is that I am out of date.
  • 00:01:35
    And these, in fact, are two behaviors that are often observed as problematic
  • 00:01:41
    when interacting with large language models. They’re LLM challenges.
  • 00:01:46
    Now, what would have happened if I'd taken a beat and first gone
  • 00:01:50
    and looked up the answer on a reputable source like NASA?
  • 00:01:55
    Well, then I would have been able to say, “Ah, okay! So the answer is Saturn with 146 moons.”
  • 00:02:03
    And in fact, this keeps changing because scientists keep on discovering more and more moons.
  • 00:02:08
    So I have now grounded my answer in something more  believable.
  • 00:02:11
    I have not hallucinated or made up an answer.
  • 00:02:13
    Oh, by the way, I didn't leak personal information about how long ago it's been since I was obsessed with space.
  • 00:02:18
    All right, so what does this have to do with large language models?
  • 00:02:22
    Well, how would a large language model have answered this question?
  • 00:02:26
    So let's say that I have a user asking this question about moons.
  • 00:02:31
    A large language model would confidently say,
  • 00:02:37
    OK, I have been trained and from what I know in my parameters during my training, the answer is Jupiter.
  • 00:02:46
    The answer is wrong. But, you know, we don't know.
  • 00:02:50
    The large language model is very confident in what it answered.
  • 00:02:52
    Now, what happens when you add this retrieval augmented part here?
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    What does that mean?
  • 00:02:59
    That means that now, instead of just relying on what the LLM knows,
  • 00:03:02
    we are adding a content store.
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    This could be open like the internet.
  • 00:03:07
    This can be closed like some collection of documents, collection of policies, whatever.
  • 00:03:14
    The point, though, now is that the LLM first goes and talks
  • 00:03:17
    to the content store and says, “Hey, can you retrieve for me
  • 00:03:22
    information that is relevant to what the user's query was?”
  • 00:03:25
    And now, with this retrieval-augmented answer, it's not Jupiter anymore.
  • 00:03:31
    We know that it is Saturn. What does this look like?
  • 00:03:35
    Well, first user prompts the LLM with their question.
  • 00:03:46
    They say, this is what my question was.
  • 00:03:48
    And originally, if we're just talking to a generative model,
  • 00:03:52
    the generative model says, “Oh, okay, I know the response. Here it is. Here's my response.”
  • 00:03:57
    But now in the RAG framework,
  • 00:04:00
    the generative model actually has an instruction that says, "No, no, no."
  • 00:04:04
    "First, go and retrieve relevant content."
  • 00:04:08
    "Combine that with the user's question and only then generate the answer."
  • 00:04:13
    So the prompt now has three parts:
  • 00:04:17
    the instruction to pay attention to, the retrieved content, together with the user's question.
  • 00:04:23
    Now give a response. And in fact, now you can give evidence for why your response was what it was.
  • 00:04:30
    So now hopefully you can see, how does RAG help the two LLM challenges that I had mentioned before?
  • 00:04:35
    So first of all, I'll start with the out of date part.
  • 00:04:38
    Now, instead of having to retrain your model, if new information comes up, like,
  • 00:04:43
    hey, we found some more moons-- now to Jupiter again, maybe it'll be Saturn again in the future.
  • 00:04:48
    All you have to do is you augment your data store with new information, update information.
  • 00:04:53
    So now the next time that a user comes and asks the question, we're ready.
  • 00:04:57
    We just go ahead and retrieve the most up to date information.
  • 00:05:00
    The second problem, source.
  • 00:05:02
    Well, the large language model is now being instructed to pay attention
  • 00:05:07
    to primary source data before giving its response.
  • 00:05:10
    And in fact, now being able to give evidence.
  • 00:05:13
    This makes it less likely to hallucinate or to leak data
  • 00:05:17
    because it is less likely to rely only on information that it learned during training.
  • 00:05:21
    It also allows us to get the model to have a behavior that can be very positive,
  • 00:05:26
    which is knowing when to say, “I don't know.”
  • 00:05:29
    If the user's question cannot be reliably answered based on your data store,
  • 00:05:35
    the model should say, "I don't know," instead of making up something that is believable and may mislead the user.
  • 00:05:41
    This can have a negative effect as well though, because if the retriever is not sufficiently good
  • 00:05:47
    to give the large language model the best, most high-quality grounding information,
  • 00:05:53
    then maybe the user's query that is answerable doesn't get an answer.
  • 00:05:57
    So this is actually why lots of folks, including many of us here at IBM,
  • 00:06:01
    are working the problem on both sides.
  • 00:06:03
    We are both working to improve the retriever
  • 00:06:06
    to give the large language model the best quality data on which to ground its response,
  • 00:06:12
    and also the generative part so that the LLM can give the richest, best response finally to the user
  • 00:06:19
    when it generates the answer.
  • 00:06:21
    Thank you for learning more about RAG and like and subscribe to the channel.
  • 00:06:25
    Thank you.
Tags
  • RAG
  • large language models
  • IBM Research
  • information retrieval
  • accuracy
  • hallucination
  • machine learning
  • current data
  • source credibility
  • AI challenges