Few-Shot Prompting Explained

Summary

The video script introduces 'F-shot prompting,' a technique to enhance the performance of large language models by providing examples or demonstrations to guide the model in understanding tasks better. It contrasts this with 'zero-shot prompting,' which relies on the model's internal knowledge without examples. The script demonstrates F-shot prompting with examples, including defining words and sentiment classification, showing how models can generate reliable responses without fine-tuning. It highlights the versatility of F-shot prompting for various tasks and its potential to address the models' limitations in understanding complex or unfamiliar tasks.

Takeaways

• 📊 F-shot prompting enhances LLMs' performance by providing examples or demonstrations.
• 🔍 Zero-shot prompting relies on the model's internal understanding without examples.
• 📝 F-shot prompting is useful when the model lacks sufficient data or task understanding.
• 🚀 By providing examples, the model better grasps the task, leading to more reliable outputs.
• 💡 F-shot prompting is especially valuable for complex tasks or underrepresented data areas.
• 📚 The process involves showing the model how to perform a task through demonstrations.
• 🛠️ F-shot prompting can be adapted for various tasks, including classification and content generation.
• 📋 The structure of prompts can vary, with different ways to design system, user, and assistant roles.
• 🧩 Examples in F-shot prompting set expectations for output, including tone, style, and content.
• 🔗 The next topic in the series will cover Chain of Thought prompting, another powerful technique.

Q & A

What is the main topic of the video?

The main topic of the video is 'F-shot prompting,' a method used to improve the performance and reliability of large language models by providing examples or demonstrations.

What is the difference between zero-shot prompting and F-shot prompting?

Zero-shot prompting involves giving an instruction to the model without any examples, assuming the model has an internal understanding of the task. F-shot prompting, on the other hand, involves providing the model with examples or demonstrations to help it understand and perform the task better.

What is an example of a task that could benefit from F-shot prompting?

An example task that could benefit from F-shot prompting is sentiment classification, where the model is given examples of text classified as either negative or positive to improve its classification accuracy.

What is the significance of providing examples in F-shot prompting?

Providing examples in F-shot prompting helps the model understand the task better and gives it a clearer expectation of the type and quality of output required, leading to more reliable and higher-quality answers.

How does the model use the examples provided in F-shot prompting?

The model uses the examples to recognize patterns and understand the task at hand. It then applies this understanding to generate responses that align with the demonstrated examples, without the need for fine-tuning the model's weights.

What is the 'GPT-3' mentioned in the script, and how does it relate to the concept of F-shot prompting?

GPT-3 is a large language model developed by OpenAI. The script discusses using GPT-3 for F-shot prompting to demonstrate how the model can generate sentences or classify text based on provided examples.

Can F-shot prompting be used for tasks other than classification or definition?

Yes, F-shot prompting can be used for various tasks, including but not limited to generating specific tones in emails, creating headlines, or defining concepts, by providing the model with relevant examples.

What is the importance of structuring the prompt correctly in F-shot prompting?

Correctly structuring the prompt in F-shot prompting is crucial as it helps the model understand the task, the input, and the expected output. It can also include indicators or delimiters to help the model distinguish between different parts of the prompt.

How does the video script demonstrate the effectiveness of F-shot prompting?

The video script demonstrates the effectiveness of F-shot prompting by showing how the model can generate a sentence using a made-up word after being provided with a demonstration of the task.

What is the next concept that the video series will cover after F-shot prompting?

The next concept that the video series will cover after F-shot prompting is 'Chain of Thought prompting,' which is another method to enhance the performance of large language models.

How can one experiment with F-shot prompting to improve model performance?

One can experiment with F-shot prompting by providing the model with various examples that are representative of the task at hand. This could include different types of inputs and corresponding outputs to help the model generalize better.

Outlines

00:00

📚 Introduction to F-Shot Prompting

This paragraph introduces the concept of F-shot prompting, a technique used to enhance the performance and reliability of large language models (LLMs). It contrasts F-shot prompting with zero-shot prompting, where models are given instructions without examples. The speaker explains that F-shot prompting involves providing examples or demonstrations to the model, which helps it understand tasks better and produce higher quality outputs. An example from the 'GPT-3' paper is used to illustrate how this technique works, showing how the model can generate a sentence using a given word after being provided with demonstrations.

05:01

🔍 Exploring F-Shot Prompting with Examples

The second paragraph delves deeper into F-shot prompting by demonstrating how to structure prompts with examples for the model to learn from. The speaker discusses the importance of providing clear examples and expectations to guide the model's output. It uses a simple task of defining words and creating sentences as an example, showing how the model can be instructed to perform classification tasks. The paragraph also touches on the potential applications of F-shot prompting, such as creating emails with a specific tone or generating headlines, and emphasizes the flexibility in designing prompts according to the task's requirements.

10:01

🛠️ Practical Application and Limitations of F-Shot Prompting

The final paragraph wraps up the discussion on F-shot prompting by highlighting its practical applications and acknowledging its limitations. It suggests that while F-shot prompting can be powerful for certain tasks, it may not always be necessary to provide both input and output examples. The speaker mentions that for some tasks, providing just the output may suffice. The paragraph concludes with a teaser for the next video, which will cover 'Chain of Thought' prompting, another method for improving LLMs' performance. The speaker thanks the viewers for watching and promises to explore more advanced prompting techniques in upcoming videos.

Keywords

💡F-shot prompting

F-shot prompting, also known as few-shot prompting, is a technique used to enhance the performance of large language models (LLMs) by providing them with examples or demonstrations of how to perform a specific task. It is a method to improve the reliability and quality of the output from these models. In the video, it is contrasted with zero-shot prompting, where no examples are given, and the model is expected to understand the task based on its internal knowledge. The script provides an example of using F-shot prompting to teach a model to create sentences using a given word, demonstrating the model's ability to learn from examples and apply that knowledge.

💡Zero-shot prompting

Zero-shot prompting is a method of interacting with LLMs where the model is given a task to perform based solely on an instruction, without any examples of how to perform the task. It relies on the model's inherent understanding and pattern recognition capabilities. The script mentions zero-shot prompting as the initial approach before introducing F-shot prompting, highlighting the limitations of zero-shot prompting when the model lacks sufficient data or understanding of a complex task.

💡Large Language Models (LLMs)

Large Language Models (LLMs) refer to advanced artificial intelligence systems designed to process and generate human-like text based on the input they receive. They are capable of performing various language tasks, such as translation, summarization, and sentiment analysis. The video discusses how F-shot prompting can be used to improve the performance of these models, particularly when they face challenges in understanding complex or unfamiliar tasks.

💡Performance

In the context of the video, 'performance' refers to the effectiveness and accuracy with which LLMs can complete a given task. F-shot prompting is introduced as a way to improve performance by providing examples that help the model understand the task better. The script illustrates this by showing how the model's performance in creating sentences can be enhanced through demonstration.

💡Reliability

Reliability, in this video, pertains to the consistency and dependability of the output produced by LLMs. F-shot prompting is presented as a method to increase reliability by giving the model demonstrations that align with the desired output, thus reducing the likelihood of errors or unexpected results.

💡Output quality

Output quality is the measure of how well the results produced by an LLM meet the expectations in terms of accuracy, relevance, and coherence. The video emphasizes the importance of output quality and how F-shot prompting can be used to ensure that the model's responses are of higher quality by learning from provided examples.

💡Classification task

A classification task is a type of problem where the model is required to categorize input data into predefined classes or labels. In the script, a classification task is demonstrated where the model is asked to classify text as either 'negative' or 'positive'. This task is used to show how F-shot prompting can guide the model to understand the classification criteria and improve its categorization accuracy.

💡Demonstration

Demonstration, within the context of the video, refers to the act of showing the LLM examples of how to perform a task. This is a core aspect of F-shot prompting, where the model is given a set of inputs and their corresponding correct outputs to learn from. The script uses the creation of sentences with a made-up word as an example of a demonstration, illustrating how the model can learn to apply this knowledge to new inputs.

💡System message

In the video, a system message is a part of the prompting structure used to communicate the task and instructions to the LLM. It is essential for setting the context and expectation for the model before it receives the user's input. The script describes how to incorporate the system message into the prompting process, emphasizing its importance in guiding the model's understanding of the task.

💡Playground

The term 'playground' in the script refers to an interactive environment or platform, such as the one provided by OpenAI, where users can experiment with and test LLMs. The video uses the playground to demonstrate how F-shot prompting is implemented in practice, showing the process of inputting examples and receiving the model's responses.

💡Chain of Thought prompting

Chain of Thought prompting is another technique mentioned in the script, which is said to be covered in a subsequent video. While not fully explained in this script, it is implied to be a powerful method for guiding LLMs to produce more logical and step-by-step responses. This technique is suggested as an alternative or additional approach to improving the model's performance.

Highlights

Introduction to the concept of F-shot prompting as a method to improve performance and reliability of large language models (LLMs).

Comparison with zero-shot prompting, where a model is given an instruction without examples.

Explanation of zero-shot prompting through a classification task example.

The limitations of zero-shot prompting in areas where the model lacks understanding or has seen insufficient data.

Introduction of F-shot prompting as a solution involving providing examples to the model for better task understanding.

Demonstration of F-shot prompting using an example from the GPD-3 paper.

The process of setting up a prompt in an interactive playground using system messages and user roles.

The importance of structuring the prompt with clear examples and expected outputs for the model.

An example of defining a word and creating a sentence using that word, showcasing the model's ability to understand and generate context.

The power of F-shot prompting in enabling models to generate sentences without fine-tuning.

The versatility of F-shot prompting for different tasks such as email tone, essay headings, or defining concepts.

The process of deleting and setting up a new example for sentiment classification.

Providing clear instructions and examples for sentiment classification to guide the model's output.

The model's response to sentiment classification, demonstrating its ability to categorize text as negative or positive.

Discussion on the model's tendency to favor certain labels based on its training data.

Strategies for providing examples to improve the model's performance in specific classifications.

The mention of Chain of Thought prompting as another powerful method to be covered in a future video.

Conclusion of the video with an invitation to the next one, highlighting the practical applications of F-shot prompting.

Transcripts

in this video we are going to go over

the concept of f shot prompting so F

shot prompting is one of the more

popular ways of prompting large language

models to be able to improve performance

and reliability in terms of the results

and output quality that we would like

from these

llms in the previous guide we covered

the idea of zero shot prompting and we

have a video for it here if you're

interested in what that concept is but

basically what we do with zero shot is

call a model or perform a task by just

giving the model an instruction so an

instruction can be classify the text

into neutral negative or positive then

you gave the model the input which is

going to be this text I think the

vacation is okay and then this output

indicator here is telling the model that

we're expecting an output which will be

one of these labels and so this you can

consider a classification task is also

considered uh zero shot prompting

because we're not adding examples right

we're not giving the model examples of

how to perform this task we are making

an assumption that the model has some

internal understanding of what this task

is and obviously this is a pattern

recognition system that

can understand what the task is and what

the intent is and be able to provide the

right answers in this case the

levels now this is a good first way of

experimenting with large language models

but these models lack capabilities in a

lot of areas and that would be for areas

where maybe they haven't seen enough

data they don't understand really the

task right it could be also a very

complex task that the model has very

little understanding of or very little

knowledge of and in those cases you may

want to experiment with something called

f shot prompting and this is what we're

going to cover now and F shot prompting

the a would be to add examples or give

the model demonstrations right you show

it how to perform the task and then by

showing it the M can understand better

what that task is about and be able to

give you more reliable and higher

quality answers so there are various

reasons why you may want to use f shot

prompting and we will get into those and

also cover an example so let's go back

to fot prompting here which is the next

guide after zero shot prompting so what

we'll do is we'll start with this simple

example and then we we'll move on to a

more interesting example so this one is

from the bral paper this is the GPD Tre

paper if I'm not mistaken and this is

directly copy pasted from that paper and

this basically tells you shows you the

idea of this few shot prompting

technique so we're going to copy this

and we're going to move it over to the

playground again the playground that I'm

using here is from open ey because we're

using the open ey models and in

particular I'm using GPD 3.5 but you

could use any of the other models that

you see available here in the playground

what I'm going to do now is I'm going to

use the system message so I can need to

provide a system message here that's

absolutely required now in the

playground so what I can do is I can

copy paste this for now but because this

requires a system message before I input

a user message what I can do is I can

actually divide this into two parts so

there are many ways how you can design

design The Prompt itself right with the

different roles like system role user

role and also the assistant role but in

this case what we want is we want to

perform this particular task and we

don't really want to add too many

instructions what we just want to do is

we just want to give them all the

examples which in this case you can see

in the examples it's making up a word

right and it's defining the word and

then it's instructing the model to come

up with an example of a sentence that

uses the word but before it does that it

gives it a demonstration on how to

perform that task so you set the

expectation basically for the model so

you can see that this particular

demonstration shows what is the input

which will be the word and what will be

the expected output which is the

sentence where that word is used that

made of word again these are made of

word so it's remarkable that this model

can come up with a sentence right on the

Fly about this particular word without

us having to find tune that model or us

having to tune the weights of that model

to tell it what this is about so that's

really the power of fot prompting so

what I'm going to do here to make this

work I can actually just go here to keep

it really simple and I'll add this as

part of user

rle so what I can do is I can just uh

add this and then now I have the system

message so this is the demonstration and

then I have this which will be the input

that makes sense for me to compose it

this way there are many other ways how

you can go about doing this you could

have also taken this and put it right

there just leave it there where I had it

and then maybe add something in the user

role some other additional instruction

uh there are different ways but I find

that this is the best way to do it uh

with these models and using these roles

so once I have that I can now run this

so you can see here it says uh let's

read the input message first to do a far

dle means to jump up and Dong really

fast an example of a sentence that uses

the word for doodle is and then from the

model we get the response which is I

couldn't contain my excitement when I

found out I won the race so I started to

fle right there on the spot so I think

it's properly using the word in a

sentence and that's great to see right

because that's what we wanted with this

particular task now this is a very

simple task we actually got this from

the paper and it was exciting at the

time when we showed that we could do

this with large language models because

this generalizes right now you can use

this uh here in this case is a toy

example but you can use this for okay if

you want a specific tone in an email you

can provide demonstrations of those

emails if you want to have certain

headlines or you want to use them all to

come up with headline suggestions for or

heading suggestions for your essays or

something like that then you can give

them all some examples of maybe previous

essay

that you think have the style that you

want and then the model should be able

to follow that to some extent right

that's the idea of f prompting you're

basically telling the model or

demonstrating to the model what you

expect in terms of the type of output

the quality of the output right the tone

perhaps as well the style of it it could

also be in this case defining of words

giving it knowledge about certain

Concepts as well there are different

ways how you can use fuchia promp this

is a basic toy example now I'm going to

delete this so let me just delete this

part here

and then what I'm going to do is I'm

going to copy over the second example we

have a second example here in the guide

it'll be recommended for you to read the

guide there there are also some

additional readings if you're interested

in going into more details but I'm

trying to give you more less a recap of

what the idea is so what I'm going to do

is I'm going to copy this over and I'm

going to paste it right here so the way

I can structure this right for every

task will be a little bit different now

I know this is uh classification task so

what I can do here is I can tell it your

task is to perform

uh sentiment

classification on the provided

inputs you will

classify the text input into either

negative or positive so that's my task I

could have improved this a bit better

but for now we will keep it as is the

instructions usually go in the system

message so I have it in the system

message already and what I can do is I

can provide it the examples here and I

could use something like this as well

that's totally fine I see a lot of

people actually use this these type of

indicators here or what we call as

delimiters or you can call it like

subheadings or whatever you may want to

call it and that's totally fine I think

that's something that the model should

be able to Leverage as well to better

understand the task and better

understand okay this part is going to be

about examples notice that I have the

examples here right so I have the input

and I have the output here right input

text and output of that input and output

of that you could also design this a

little bit different now I wanted to

take a shortcut here because the way how

I'm inputting this information is going

to be with this you know

additional uh indicator here right the

additional characters but I could also

use something like input text and then

output right so I could I could change

the way I'm formatting these examples

and then I will have to carry that over

to my final input that I'm providing for

the model to classify so here I'm just

going to classify this and so we got

negative from the model which is exactly

the classification we expected now even

this is a very simple task but as soon

as you start to scale on tasks like this

this model does tend to favor a certain

label here or a certain category like

maybe would favor positive or negative

because it has seen potentially more

positive right type of content in its

training data so our task now is to sort

of figure out what is the best strategy

to provide these models the examples

maybe the model is lacking the ability

to perform a specific classification

then in that case we can give it more

examples of those so that it gets it

right now this case you saw that I give

it an input output that's not always

required you don't always have to give

it input output you can also give it

output some Tas for instance where maybe

you want a specific type of email right

in a specific tone in that case you can

just give it the email right you don't

really need to give it an input in that

case so some task would require input

output but some you know you can just

get away with just giving it the output

that'll be it for this demo and

hopefully it's more clear what this is

about um feel free to read here again

understand also the limitations in the

next video we are also going to cover

Chain of Thought prompting which is

another really powerful way of prompting

these models that will be it for this

particular video thanks for watching and

catch you in the next one