Master Prompting Concepts: Zero-Shot and Few-Shot Prompting

As we go further into the art of prompting there are some major techniques that can assist you as a prompt engineer. The four major ones are:

1. Zero-shot prompting
2. Few-shot prompting
3. Fine-tuning
4. Embedded Vector Search aka Embedding

The following graphic briefly summarises these approaches and when some you may need to explore them.

Zero-Shot Prompting

Language models, especially large-scale ones like GPT-4, have revolutionized the way we approach natural language processing tasks. One of the most remarkable features of these models is their ability to perform zero-shot learning.

This means that the models can understand and execute tasks without having seen any explicit examples of the desired behaviour. In this section, we will explore the concept of zero-shot prompting and provide unique examples to illustrate its capabilities.

Example 1:

Prompt: Translate the following English text into French.

Text: The weather is beautiful today.

Output: Le temps est magnifique aujourd'hui.

Example 2:

Prompt: Summarize the main idea in the following text.

Text: The rapid growth of technology has led to significant advancements in various industries. From communication and transportation to healthcare and education, technology has played a crucial role in improving our lives. However, we must also be cautious of the potential negative effects, such as job loss due to automation and privacy concerns.

Output: Technology's rapid growth has positively impacted various industries but also raises concerns about job loss and privacy.

In the examples above, the model is given clear instructions without any demonstrations or examples. The zero-shot capability of the model allows it to understand the task and generate appropriate outputs.

However, it's essential to note that zero-shot prompting may not always yield accurate or desired results. In such cases, few-shot prompting can be a more effective approach. By providing the model with demonstrations or examples, it can better understand the desired output and perform the task more accurately. In the next section, we delve into the concept of few-shot prompting and discuss its applications.

Few-Shot Prompting

Few-shot prompting enables large language models to perform better on complex tasks by providing demonstrations, but it has limitations in addressing certain reasoning problems, suggesting the need for advanced prompt engineering and alternatives like chain-of-thought prompting.

While zero-shot capabilities have shown remarkable results, few-shot prompting has emerged as a more effective way to tackle complex tasks by utilizing different numbers of demonstrations, such as 1-shot, 3-shot, 5-shot, and so on.

What is Few-Shot Prompting

Few-shot prompting is a technique used to guide large language models (LLMs), like GPT-3, towards generating desired outputs by providing them with a few examples of input-output pairs. While few-shot prompting has shown promising results, there are limitations to this approach. This method allows for in-context learning by conditioning the model using examples, guiding it to produce better responses.

Examples and Explanations of Few-Shot Prompting

In this section, we will delve into more detailed examples of few-shot prompting to better understand how it works and how it can be applied effectively.

Example 1: Using a New Word in a Sentence

To demonstrate few-shot prompting, consider the following example from Brown et al. (2020) in which the task is to use a new word in a sentence correctly:

Prompt:

A "whatpu" is a small, furry animal native to Tanzania. An example of a sentence that uses the word whatpu is: We were travelling in Africa and we saw these very cute whatpus. To do a "farduddle" means to jump up and down really fast. An example of a sentence that uses the word farduddle is:

AI:

When we won the game, we all started to farduddle in celebration.

Here, the model has learned to perform the task by providing it with just one example (1-shot).

Example 2: Sentiment Analysis with Random Labels

In this example, we will experiment with random labels for a sentiment analysis task:

Prompt:

This is awesome! // Negative This is bad! // Positive Wow that movie was rad! // Positive What a horrible show! //

AI:

Negative

Despite randomizing the labels, the model still produces the correct answer. This demonstrates the importance of format in few-shot prompting.

Example 3: Complex Reasoning Task

Few-shot prompting may struggle with tasks that require complex reasoning. For example, consider the following problem:

Prompt:

The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1.

AI:

Yes, the odd numbers in this group add up to 107, which is an even number.

The response is incorrect, indicating that few-shot prompting may not be sufficient for this type of problem. Let's try to add more examples:

Prompt:

The odd numbers in this group add up to an even number: 4, 8, 9, 15, 12, 2, 1. A: The answer is False.

The odd numbers in this group add up to an even number: 17, 10, 19, 4, 8, 12, 24. A: The answer is True.

The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1. A:

AI:

The answer is True.

Again, the response is incorrect. This example demonstrates that few-shot prompting might not be enough to generate reliable responses for complex reasoning tasks. Instead, it might be more effective to use advanced prompting techniques, such as chain-of-thought prompting, which involves breaking a problem down into smaller steps and demonstrating those steps to the model.

These examples illustrate the potential of few-shot prompting as a technique to improve the performance of large language models on various tasks. However, as the complex reasoning task example shows, it may not always be sufficient, prompting the need for advanced techniques like chain-of-thought prompting.

Tips for Effective Few-Shot Prompting

1. Label Space: Ensure the label space is well-defined and relevant for the task at hand, as it significantly affects the performance of few-shot prompting. Example Task: Sentiment analysis (Positive or Negative)
   Well-defined Label Space: Positive, Negative
   Example: "The movie was fantastic!" should be labeled as Positive.
2. Input Text Distribution: Carefully consider the distribution of input text in the demonstrations, as it plays a crucial role in the model's ability to generalize to unseen examples.
   Example Task: Classify animals into categories (Mammal, Bird, Fish, Reptile, or Amphibian)
   Balanced Input Text Distribution: Provide an equal number of examples for each category in the demonstrations.
   Example: "Kangaroo, Mammal; Penguin, Bird; Salmon, Fish; Iguana, Reptile; Frog, Amphibian."
3. Format Consistency: Maintain a consistent format in the demonstrations, as it helps the model understand the task and generate better responses.
   Example
   Task: Translate English sentences to French
   Consistent Format: English sentence // French translation
   Example: "Hello, how are you? // Bonjour, comment ça va?"
4. Random Labels: Interestingly, using random labels can lead to better performance than not providing any labels at all. This suggests that even with incorrect labels, the model can still learn from the structure and format of the demonstrations.
   Example
   Task: Sentiment analysis (Positive or Negative)
   Random Labels Example: Assign random labels to the input text and observe if the model can still generate correct answers.
   Demonstration: "This is amazing! // Negative; What a terrible day! // Positive;"
   Input: "I had a great time at the party."
   Output: Even with random labels, the model may still classify the sentiment as Positive.
5. True Distribution of Labels: Select random labels from a true distribution of labels (instead of a uniform distribution), as it can help the model better understand the problem and improve its performance.
   Example
   Task: Categorize news articles into topics (Politics, Sports, Technology, or Entertainment)
   True Distribution Example: Suppose the true distribution of labels is 40% Politics, 30% Sports, 20% Technology, and 10% Entertainment. Select random labels based on this distribution, rather than uniformly.
   Demonstration: "Politics, Article A; Sports, Article B; Politics, Article C; Technology, Article D;"
   Input: "Article E"
   Output: The model is more likely to generate accurate categorizations based on the true distribution of labels.

These tips, based on the findings of Min et al. (2022), can help enhance the effectiveness of few-shot prompting and enable AI models to generate more accurate and coherent responses.

When to Use Zero-Shot & Few-Shot Prompting

Here are some of the major instances where you would want to use the few-shot prompting methodology:

1. Zero-shot prompting is insufficient: Large language models may have impressive zero-shot capabilities, but they can still struggle with more complex tasks. Few-shot prompting can help improve the model's performance on these tasks by providing additional context and examples.
2. Limited training data is available: If you have a limited amount of labelled data for a specific task, few-shot prompting can help the model learn more effectively by leveraging the existing demonstrations.
3. Fine-tuning is not feasible: When fine-tuning the model is not possible due to computational or data constraints, few-shot prompting can serve as an alternative to enhance the model's performance on a particular task.
4. Rapid experimentation is needed: Few-shot prompting allows for quick experimentation, as it only requires a few examples to demonstrate the desired behaviour. This can help you iterate and test ideas more quickly than with other techniques.

Likely Use Cases for Few-Shot Prompting

Few-shot prompting has a wide range of applications across various domains, owing to its ability to improve large language model performance on complex tasks with limited examples. Some of the most likely use cases for few-shot prompting include:

1. Natural Language Understanding (NLU): Few-shot prompting can be used to enhance NLU tasks such as sentiment analysis, entity recognition, and relationship extraction. By providing a few examples of the desired behavior, models can better understand and classify text based on context and specific requirements.
2. Question Answering (QA): In the context of QA systems, few-shot prompting can help improve the model's ability to generate accurate and relevant answers to user queries by providing demonstrations of correct answers for similar questions.
3. Summarization: Few-shot prompting can be applied to improve text summarization by providing examples of well-summarized content. This can help guide the model to generate more concise and informative summaries.
4. Translation: For translation tasks, few-shot prompting can be employed to adapt large language models to specific translation styles or specialized domains with limited examples of the translated text.
5. Code Generation: Few-shot prompting can be used to enhance code generation tasks by providing demonstrations of the desired output for a given input. This can help the model generate more accurate and efficient code based on the provided context.
6. Creative Writing and Content Generation: Few-shot prompting can be applied to creative writing and content generation tasks, such as generating stories, articles, or marketing copy, by providing examples of the desired writing style, tone, or structure.
7. Domain-specific Tasks: Few-shot prompting can be especially useful in niche domains where data is limited or costly to acquire. By providing a few examples of the desired output, models can be guided to perform tasks in specialized fields such as finance, law, medicine, or scientific research.
8. Conversational AI: In the context of chatbots and conversational AI, few-shot prompting can be used to guide the model's responses to user queries, making them more context-aware, accurate, and coherent. By providing examples of desired conversational patterns, the model can generate more human-like interactions.
9. Structured Output Generation: Few-shot prompting can be particularly useful for applications where the large language model's output must adhere to a specific format, contain particular information, or follow certain patterns. By providing demonstrations of the desired output structure, models can generate responses that meet these specific requirements. Some examples include:
10. Data Extraction and Formatting: In tasks where information must be extracted from unstructured text and presented in a structured format (e.g., tables, lists, or key-value pairs), few-shot prompting can be used to guide the model in generating the desired output. Examples of formatted output can help the model understand the structure it should adhere to while extracting and organizing the relevant information.
11. Template-based Content Generation: When generating content based on specific templates, such as legal documents, contracts, or business reports, few-shot prompting can help ensure that the model generates text that complies with the required format, structure, and language. Providing examples of properly formatted documents can help the model generate content that adheres to the established norms of the specific domain.
12. Customized Reporting and Visualization: In applications involving the generation of customized reports or visualizations from raw data, few-shot prompting can be used to guide the model in presenting the information in a specific format or layout. By providing examples of desired report structures or visualization styles, the model can generate outputs that meet the user's requirements and preferences.

These use cases represent just a fraction of the potential applications for few-shot prompting. As large language models continue to evolve and improve, we can expect to see even more innovative applications for few-shot prompting across various industries and domains.

Implementing Few-Shot Methodology within the Framework of Prompt Recipes

Prompt recipes provide a structured approach to creating and reusing effective prompts for AI applications, ensuring consistent and repeatable results. To incorporate few-shot methodology within this framework, we need to consider the four key components of a prompt recipe: Task, Instructions, Context, and Parameters/Settings. Here's how few-shot prompting can be integrated into each component:

1. Task: Clearly define the task you want the AI model to perform, focusing on the specific problem it should solve or the outcome it should achieve. With few-shot prompting, this task should be one that benefits from the addition of demonstrations or examples.
2. Instructions: Provide clear and concise instructions to the model, outlining the desired behavior or output. For few-shot prompting, this should include one or more demonstrations of the task, illustrating how the model should perform the task based on the given examples. Ensure that the examples provided are representative of the task and cover diverse cases, if possible.
3. Context: In the context component of the prompt recipe, include any background information, domain-specific knowledge, or relevant details that will help the model understand the task more effectively. When using few-shot prompting, the context may include the examples themselves, as well as any additional information that can help the model generate accurate and contextually appropriate responses.
4. Parameters/Settings: Fine-tune the parameters and settings of the AI model to optimize its performance for the given task. For few-shot prompting, this might involve adjusting the number of shots (examples), experimenting with different input and output formats, or tweaking the model's temperature and token limit to control the randomness and length of the generated response.

Example: Sentiment Analysis Using Few-Shot within a Prompt Recipe

In this example, we will implement a few-shot prompting methodology for a sentiment analysis task using the prompt recipe framework. The goal is to guide the AI model to classify input sentences as either Positive or Negative.

1. Task: Sentiment analysis - classify sentences as Positive or Negative.
2. Instructions: a. Clearly instruct the model to determine whether the input sentence has a positive or negative sentiment. b. Provide a few-shot demonstration with examples of both positive and negative sentences, along with their respective sentiment labels.
3. Context: None required, as the examples provided within the instructions serve as sufficient context for the model to understand the sentiment analysis task.
4. Parameters/Settings: For this example, we will use the default settings. However, you could experiment with temperature and token limit settings based on your specific requirements.

Sample Prompt:

Classify the sentiment of the following sentences as Positive or Negative:

Example 1: Sentence: I love this product. Sentiment: Positive

Example 2: Sentence: The food was terrible. Sentiment: Negative

Example 3: Sentence: The concert was amazing. Sentiment: Positive

Example 4: Sentence: I had a terrible experience with customer support. Sentiment: Negative

Sentence to classify: The movie was incredibly boring.

Expected Output:

Sentiment: Negative

In this example, the few-shot prompt recipe provides the AI model with a clear task (sentiment analysis) and instructions that include demonstrations of the desired output. By incorporating few-shot methodology within the prompt recipe framework, the AI model is guided to generate a more accurate classification for the given input sentence.

Example: Solving Basic Arithmetic Word Problems Using Few-Shot within a Prompt Recipe

In this example, we will implement a few-shot prompting methodology for solving basic arithmetic word problems using the prompt recipe framework. The goal is to guide the AI model to perform the appropriate calculations and provide the correct answer.

1. Task: Solve basic arithmetic word problems.
2. Instructions:a. Clearly instruct the model to read the word problem and determine the required arithmetic operation to solve it. b. Provide a few-shot demonstration with examples of different word problems, along with their respective solutions.
3. Context: None required, as the examples provided within the instructions serve as sufficient context for the model to understand the arithmetic word problem-solving task.
4. Parameters/Settings: For this example, we will use the default settings. However, you could experiment with temperature and token limit settings based on your specific requirements.

Sample Prompt:

Solve the following arithmetic word problems:

Example 1: Problem: If John has 15 apples and he gives 6 apples to his friend, how many apples does he have left? Solution: 9 apples

Example 2: Problem: A pizza is divided into 8 equal slices. If Sarah ate 3 slices, how many slices are left? Solution: 5 slices

Example 3: Problem: Tom earns $50 per day. How much will he earn in 7 days? Solution: $350

Problem to solve: Emma bought 4 notebooks, each costing $3. How much did she spend in total?

Expected Output:

$12

In this example, the few-shot prompt recipe provides the AI model with a clear task (solving basic arithmetic word problems) and instructions that include demonstrations of the desired output. By incorporating few-shot methodology within the prompt recipe framework, the AI model is guided to generate a more accurate solution for the given word problem.

Example: Finding the Median of a Set of Numbers Using Few-Shot within a Prompt Recipe

In this example, we will implement a few-shot prompting methodology for finding the median of a set of numbers using the prompt recipe framework. The goal is to guide the AI model to perform the appropriate calculations and provide the correct answer. This task is more complex and might be challenging for GPT-3 without the help of examples.

1. Task: Find the median of a set of numbers.
2. Instructions:a. Clearly instruct the model to calculate the median of the given set of numbers by sorting them in ascending order and then finding the middle number (or the average of the two middle numbers if there are an even number of values). b. Provide a few-shot demonstration with examples of different sets of numbers, along with their respective medians.
3. Context: None required, as the examples provided within the instructions serve as sufficient context for the model to understand the median calculation task.
4. Parameters/Settings: For this example, we will use the default settings. However, you could experiment with temperature and token limit settings based on your specific requirements.

Sample Prompt:

Find the median of the following sets of numbers:

Example 1: Numbers: [3, 1, 4] Median: 3

Example 2: Numbers: [12, 7, 3, 9] Median: 8

Example 3: Numbers: [5, 7, 2, 10, 6] Median: 6

Set of numbers to find the median: [11, 23, 5, 19, 8, 14]

Expected Output:

Median: 12.5

In this example, the few-shot prompt recipe provides the AI model with a clear task (finding the median of a set of numbers) and instructions that include demonstrations of the desired output. By incorporating a few-shot methodology within the prompt recipe framework, the AI model is guided to generate a more accurate solution for the given problem, which might be difficult for GPT-3 to solve without the help of examples.

Prompt Recipe Example: Converting Informal Sentences to a Formal Writing Style Using Few-Shot Prompts

1. Task: Convert informal sentences to a formal writing style.
2. Instructions:a. Clearly instruct the model to rewrite the given informal sentence in a formal writing style by avoiding contractions, and colloquialisms, and addressing the reader directly. b. Provide a few-shot demonstration with examples of informal sentences and their respective formal versions.
3. Context: None required, as the examples provided within the instructions serve as a sufficient context for the model to understand the task of converting sentences to a formal writing style.
4. Parameters/Settings: For this example, we will use the default settings. However, you could experiment with temperature and token limit settings based on your specific requirements.

Sample Prompt:

Rewrite the following informal sentences in a formal writing style:

Example 1: Informal: Hey, what's up? I just wanted to let you know I won't be able to make it today. Formal: Greetings, I regret to inform you that I will not be able to attend the event today.

Example 2: Informal: The movie was pretty cool, but the ending was kinda confusing. Formal: The film was rather impressive; however, the conclusion was somewhat perplexing.

Example 3: Informal: I gotta say, this new phone's camera is sick! Formal: I must admit, the camera on this new phone is quite remarkable.

Informal sentence to rewrite: It's really hard to find a good place to eat around here, don't you think?

Expected Output:

Formal: It is quite challenging to locate a satisfactory dining establishment in this area, would you not agree?

In this example, the few-shot prompt recipe provides the AI model with a clear task (converting informal sentences to a formal writing style) and instructions that include demonstrations of the desired output. By incorporating few-shot methodology within the prompt recipe framework, the AI model is guided to generate accurate conversions of informal sentences to a more formal writing style.

Prompt Recipe Example: Rewriting Sentences in Pirate Speak Using Few-Shot Prompts

1. Task: Convert regular sentences into pirate speak.
2. Instructions: Clearly instruct the model to rewrite the given sentence in pirate speak by using pirate vocabulary, phrases, and speech patterns. b. Provide a few-shot demonstration with examples of regular sentences and their respective pirate-speak versions.
3. Context: None required, as the examples provided within the instructions serve as a sufficient context for the model to understand the task of converting sentences to pirate speak.
4. Parameters/Settings: For this example, we will use the default settings. However, you could experiment with temperature and token limit settings based on your specific requirements.

Sample Prompt:

Rewrite the following sentences in pirate speak:

Example 1: Regular: Hello, how are you today? Pirate: Ahoy, how be ye this fine day?

Example 2: Regular: I need to go to the store to buy some groceries. Pirate: I be needin' to sail to the market to fetch some grub.

Example 3: Regular: Can you help me find my lost treasure? Pirate: Can ye lend a hand in seekin' me missin' booty?

Regular sentence to rewrite: We will meet at the dock at sunset.

Expected Output:

Pirate: We be meetin' at the pier when the sun kisses the horizon.

In this example, the few-shot prompt recipe provides the AI model with a clear task (converting regular sentences to pirate speak) and instructions that include demonstrations of the desired output. By incorporating few-shot methodology within the prompt recipe framework, the AI model is guided to generate sentences in pirate speak that capture the essence of the original sentence while employing pirate vocabulary, phrases, and speech patterns.

Performance of Few-Shot Prompting with Different LLMs

Few-shot prompting generally performs better with more advanced and larger language models, as these models have been trained on more extensive and diverse data, enabling them to better generalize and adapt to new tasks. Some of the most prominent large language models that have shown promising results with few-shot prompting include:

1. GPT-3/3.5/4 (OpenAI): GPT-3, with its 175 billion parameters, has demonstrated remarkable performance in few-shot prompting. Its ability to understand the context and generate coherent responses makes it one of the best choices for this technique.
2. BERT (Google): BERT is another large language model that can be effectively used with few-shot prompting, especially when fine-tuned for specific tasks.
3. T5 (Google): The Text-to-Text Transfer Transformer (T5) is a versatile large language model that has shown strong performance across various tasks when used with few-shot prompting.
4. XLNet (Google/CMU): XLNet is another large language model that can benefit from few-shot prompting, particularly for tasks like question answering and language understanding.

While there seem to be new LLMs being released every week, ultimately, the choice of a large language model for few-shot prompting will depend on factors such as the specific task, data availability, computational resources, and the desired level of performance. It is essential to experiment with different models and settings to find the best fit for your particular use case.

Limitations of Few-Shot Prompting

Despite its potential, few-shot prompting has limitations, particularly when dealing with complex reasoning tasks.

In this section, we will discuss the main challenges and drawbacks of few-shot prompting:

1. Inconsistency: Few-shot prompting can sometimes yield inconsistent or unpredictable results. The model may generate high-quality responses in some instances while producing irrelevant or inaccurate outputs in others. The limited number of examples provided may not be sufficient to guide the model consistently.
2. Sensitivity to Prompts: The performance of few-shot prompting is highly dependent on the quality and format of the prompts. Poorly designed prompts can lead to suboptimal results, and finding the most effective prompt can be a time-consuming and iterative process.
3. Confabulation: Few-shot prompting may result in the model generating plausible-sounding but incorrect or fabricated information. This confabulation can make it challenging to rely on the model's outputs for critical applications, where accuracy and reliability are paramount.
4. Lack of Fine-Grained Control: Few-shot prompting provides limited control over the model's generated outputs. While fine-tuning allows for more precise adjustments to the model's parameters, few-shot prompting relies on guiding the model through examples, which may not always produce the desired level of control.
5. Scalability: Few-shot prompting can be resource-intensive, especially when working with large language models. The computational power required for processing multiple input-output examples may limit the scalability of this approach for large-scale or real-time applications.
6. Inability to Learn New Information: Unlike fine-tuning, few-shot prompting does not inherently teach the model new information. It only guides the model to generate outputs based on the patterns it has already learned. This limitation can restrict the adaptability of the model to new domains or evolving requirements.

Few-shot prompting is indeed a powerful technique for guiding LLMs, but it has its limitations. Understanding these challenges is essential for selecting the most appropriate method for a given task and for developing strategies to address the inherent drawbacks of few-shot prompting.

Opting for Fine-Tuning When Few-Shot Prompting Falls Short

In some instances, few-shot prompting may not effectively address a specific use case or deliver the desired results. In such situations, fine-tuning becomes a more suitable option to tailor the large language model (LLM) to the targeted application.

Few-shot prompting relies on providing the model with a handful of examples as context, allowing it to understand the desired output format and perform the task accordingly. While this approach can work well for many applications, certain tasks may require more nuanced understanding or domain-specific knowledge that few-shot prompting struggles to provide.

Fine-tuning, on the other hand, involves adjusting the parameters of a pre-trained model to improve its performance on a particular task. By supplying the model with a curated dataset of relevant examples, fine-tuning allows the LLM to generate more accurate and context-specific responses. This process is especially beneficial for tasks that demand a deeper understanding of domain-specific terminology, jargon, or unique context that may not be sufficiently captured through few-shot prompting.

When considering fine-tuning as an alternative to few-shot prompting, it is crucial to acknowledge that this approach can be more time-consuming, computationally intensive, and expensive. However, when the need arises to tailor an LLM to a specific domain or task with greater precision, the benefits of fine-tuning often outweigh these challenges. This makes fine-tuning an essential technique for organizations and researchers aiming to leverage the full potential of LLMs, such as GPT-3, in their unique applications.

Takeaway

Few-shot prompting is a valuable technique that improves large language models' performance on complex tasks, but it has its limitations. For more reliable responses on problems that involve multiple reasoning steps, advanced prompting techniques like chain-of-thought prompting should be considered. Additionally, fine-tuning the models and further experimenting with prompt engineering can lead to even better outcomes.