Zero Shot Prompting vs. Few-Shot Prompting: Techniques and Real-World Applications

Understanding Zero-Shot Prompting

When comparing the effectiveness of a large language model (LLM), we focus on the results. However, the quality of the output depends heavily on the quality of the input. This is where prompt engineering comes in, where engineers attempt to design prompts to get the desired output.

Zero-shot prompting is a technique where we don’t provide the model with any examples. The prompt to the model consists solely of a description of the task or question, and the model must rely on its pre-trained knowledge to respond appropriately.

In traditional approaches to prompt engineering, task-specific training and fine-tuning are often used on labeled examples. Let’s say we have an image of a fish. Traditionally, we would train our model by giving it multiple pictures of fish and instructing the model that these images represent fish. We may also give the model pictures of other animals and tell it that these animals are not fish.

In zero-shot prompting, we don’t give the model any pictures of fish beforehand. In our prompt, we can simply show a picture of a fish and say, “This animal has gills, scales, and lives underwater. What animal is this?” Even if the model has not been trained on a specific species, it can still use its general knowledge of animals and our own description of the animal to identify it.

Understanding Few-Shot Prompting

With few-shot prompting, we provide a few examples in the prompt to help the model generate a more accurate result. For instance, we would give some pictures of fish in the prompt and then ask the model to identify a picture of a fish.

Here, the prompt includes on-the-fly training data, and the model attempts to find patterns that will enable it to infer how it should respond to the query. Few-shot prompting generally yields better accuracy, especially on complex tasks, because the examples help clarify the task requirements and format.

Studies have shown that larger models especially benefit from this approach. As the model size increases, the gap between zero-shot and few-shot performance often grows since larger models are better at recognizing and generalizing patterns from a few in-context examples.

Real-World Applications of Zero-Shot Prompting

Zero-shot prompting has gained popularity due to its versatility across a wide range of applications. Without requiring labeled examples, it can excel at tasks across different domains, especially when few-shot prompting may not be feasible or optimal.

Text Generation and Summarization

Whether it’s summarizing a long research paper or drafting an email, large language models are often used to produce a variety of written content through zero-shot prompts [1]. Some common prompts may be “Translate this sentence to French” or “Write a short story about a trip to Japan,” and the model will use its broad knowledge of languages and genres to perform these tasks. Zero-shot prompting is often used in content creation and creative writing, as models can also adapt to various styles and topics.

Classification

Zero-shot prompting is also effective for classification tasks [2]. The goal of these tasks is to assign labels or categories to text or images. It is commonly used in sentiment analysis, where we want the model to classify a sentence as positive, negative, or neutral. This can often be used to classify reviews as either positive or negative, or label emails as “urgent” or “not urgent.”

Zero-shot prompting is valuable when labeled examples are scarce or when speed is important. The model doesn’t have to process example inputs or require fine-tuning, so a smaller prompt size can lead to lower latency. Companies often use this for customer feedback, detecting spam, and analyzing social media sentiment.

Question Answering

With zero-shot prompting, we simply ask the model questions. We can ask factual questions, such as “What is the capital of the United States?” or explanatory questions such as “Why is sleep important?” The model isn’t specifically trained on a QA dataset; instead, it utilizes its existing knowledge and reasoning ability to generate an answer.

Many companies have become virtual assistants and chatbots in their customer support services. Clearly, the accuracy of the answers that the chatbots give depends on whether the required information was present in the model’s training data and understood correctly. Especially with factual questions such as “What year was George Washington born?”, the model wouldn’t know the answer unless it was trained on it.

However, just because the model was trained on relevant data, it doesn’t guarantee that the model will retrieve the correct fact and present it accurately. The art of prompt engineering involves writing prompts to retrieve the desired output from the model.

Best Practices for Zero-Shot Prompting

Since designing prompts is so essential, how do we write with a prompt that yields the result that we want?

Specific Instructions

Always write prompts that describe the task and desired output. Refrain from giving vague or open-ended prompts because they can confuse the model. Include details or constraints to help the model know the type of answer it should give. For example, instead of asking the model, “Explain deep learning,” you might say, “Explain the core concepts of deep learning to a software engineer familiar with machine learning but new to deep learning frameworks.”

Provide Context

If a task could be interpreted in multiple ways, provide context in the prompt to narrow it down. Role prompting is a technique where the model plays a role or persona so that it can give more context-specific answers. For example, instead of simply asking, “What is Italy known for?” we could say, “You are an expert travel guide. Which cities and attractions should I visit in Italy in the summer?”

Refine the Prompt

The first prompt attempt may not yield perfect results. Zero-shot prompting may be more sensitive to wording, so even slight changes in phrasing or format can have a significant impact on the output [3]. If the model’s first answer is incorrect, try rephrasing the instructions or adding additional clarifications to guide it toward what you want.

Few-Shot Prompting

Zero-shot prompting won’t always work. If the model consistently misunderstands the task or produces low-quality outputs, consider moving on to a few-shot approach. See if adding one or two examples improves the result. Demonstrations can quickly teach the model the correct pattern, and few-shot prompting may be needed, especially with more complex and challenging tasks [4].

Challenges

Despite its convenience and flexibility, zero-shot prompting still comes with its limitations and challenges.

Limitations of Zero-Shot Prompting

Since no examples are given, the model assumes what the task should be. For simple tasks, this would be fine, but for complex tasks, the model can easily make mistakes or give incomplete answers. The model might misinterpret what we actually want. Without proper context, any ambiguity in the request can lead to unexpected responses.

As we mentioned earlier, the model cannot provide accurate answers to questions where it lacks the necessary knowledge. The model may not have been trained on certain niche topics or recent events. The model's performance on a specific query will depend on the extent of its pre-training. Zero-shot prompting does not guarantee that the model can perform any arbitrary task.

Limitations of Few-Shot Prompting

Few-shot prompting has more examples, so it needs longer prompts, which may be limited by the model’s context window. More examples also lead to more tokens, which in turn result in higher computation costs.

The model’s performance also depends on the quality of the examples that are provided. Even with higher costs, there is no guarantee that the model will produce the desired output. The model still might misinterpret instructions or fail to recognize patterns.

Future Directions and Emerging Trends

Instruction Tuning

With the rise of zero-shot prompting, large language models are constantly refined through instruction tuning. In instruction tuning, LLMs are trained or fine-tuned on examples of natural language tasks by giving them sample prompts and correct answers. The goal is to understand zero-shot instructions and also improve the results on unseen tasks. As models improve, they become increasingly adept at following instructions, blurring the line between “pre-training” and “prompting” as they are also being trained to excel at prompting.

Chain-of-Thought Reasoning

Chain-of-thought reasoning is a technique in prompt design where the model produces step-by-step reasoning before giving the final answer. Simply adding the phrase “Let’s think step by step” to the prompt can encourage models to break down complex problems, which has been shown to improve accuracy for more challenging tasks.

Multimodal AI Models

Modern AI models don’t handle just text, but also images and audio. Recently, Sora has gained popularity for generating images from simple text descriptions. As models advance, zero-shot prompting may be applied to more complex tasks, such as looking at a visual chart and reporting key insights in a paragraph. Combining text and images creates much higher complexities, but as AI learns how to adapt to a wide variety of tasks, we are seeing the promise of zero-shot prompting.

Conclusion

Zero-shot prompting has transformed how we interact with AI models. A single model can tackle numerous tasks with nothing more than a single instruction. The potential of both zero-shot and few-shot prompting displays the flexibility of large language models. By understanding the strengths and weaknesses of each, we will know when to use each technique to receive our desired result and to further innovation in the years ahead.

References

[1] Wanyoike, Muthoni. “Implementing Text Summarization Using OpenAI’s GPT-3 API.” Medium, March 2, 2023. https://medium.com/muthoni-wanyoike/implementing-text-summarization-using-openais-gpt-3-api-dcd6be4f6933

[2] Yin, Wenpeng, Jamaal Hay, and Dan Roth. Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach. In Proceedings of EMNLP-IJCNLP, 2019.

[3] Zhao, T. Z., Wallace, E., Feng, S., Klein, D., & Singh, S. (2021). Calibrate Before Use: Improving Few-Shot Performance of Language Models.

[4] Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022). Large Language Models are Zero-Shot Reasoners.