- The blog article explains how to use Llama 2, a large language model, for text classification tasks using prompt engineering, a technique that guides LLMs to perform specific tasks with simple and effective prompts.
- The blog article provides a step-by-step guide on how to design, test, and evaluate a prompt for text classification using Llama 2, using the AG News dataset as an example.
Text classification is a common natural language processing (NLP) task that involves assigning one or more labels to a given piece of text. For example, we might want to classify customer reviews into positive, negative, or neutral categories, or classify news articles into different topics such as politics, sports, or entertainment.
Text classification can be done using various methods, such as rule-based systems, traditional machine learning algorithms, or deep learning models. However, one of the most recent and powerful approaches is to use large language models (LLMs) such as Llama 2.
Llama 2 is an open-source LLM developed by Meta AI that can generate natural and fluent text based on a given prompt. It can also perform various NLP tasks such as summarization, translation, question answering, and text classification. Llama 2 is available in different sizes, ranging from 7 billion to 70 billion parameters, and has a context length of 4096 tokens, which means it can process longer texts than most other LLMs.
In this blog post, we will show you how to use Llama 2 for text classification tasks using a simple and effective technique called prompt engineering. We will also provide some tips and tricks to improve the performance and accuracy of your text classifier.
What is Prompt Engineering?
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Prompt engineering is the art and science of designing prompts that guide LLMs to perform specific tasks. A prompt is a piece of text that provides some instructions and examples to the LLM, as well as a placeholder for the input text and the desired output. For example, here is a possible prompt for text classification:
A message can be classified as one of the following categories: book, cancel, change. Examples: - Book: "I would like to book a room for two nights." - Cancel: "Please cancel my reservation and refund the payment." - Change: "I need to change the dates of my booking to next week." Based on these categories, classify this message: "I would like to cancel my booking and ask for a refund."
The prompt tells the LLM what the task is, what the possible categories are, and provides some examples of each category. It also provides a placeholder for the input message and asks the LLM to classify it. The expected output would be something like:
Prompt engineering is a powerful technique because it allows us to leverage the general knowledge and language skills of LLMs without having to fine-tune them on specific datasets or domains. This can save time and resources, as well as avoid potential issues such as data privacy, data scarcity, or data bias.
However, prompt engineering also requires some creativity and experimentation, as different prompts may lead to different results. There is no one-size-fits-all solution for prompt engineering, and the optimal prompt may depend on various factors such as the task, the domain, the input format, the output format, the LLM size, and the LLM variant.
How to Prompt Llama 2 for Text Classification?
In this section, we will show you how to prompt Llama 2 for text classification using a real-world example. We will use the AG News dataset, which consists of news articles from four categories: World, Sports, Business, and Sci/Tech. Our goal is to build a text classifier that can assign one of these categories to a given news article.
To prompt Llama 2 for text classification, we will follow these steps:
- Choose a Llama 2 variant and size.
- Define the categories and provide some examples.
- Format the input and output texts.
- Test and evaluate the prompt.
Step 1: Choose a Llama 2 variant and size
Llama 2 comes in two variants: base and chat. The base variant is trained on publicly available online data sources, while the chat variant is fine-tuned on chat-style interactions using supervised learning and reinforcement learning from human feedback. The chat variant is more suitable for conversational tasks such as dialogue generation or question answering, while the base variant is more suitable for non-conversational tasks such as summarization or text classification.
Llama 2 also comes in different sizes: 7B, 13B, 34B (not released), and 70B parameters. The larger the size, the more powerful and accurate the model is, but also the more computationally expensive and memory-intensive it is. Therefore, choosing the right size depends on your available resources and your performance requirements.
For our example, we will use the base variant of Llama 2 with 7B parameters. This is because we want to perform a non-conversational task with a reasonable trade-off between accuracy and efficiency. You can download this model from Meta’s website or use Hugging Face’s converted version.
Step 2: Define the categories and provide some examples
The next step is to define the categories that we want to use for text classification and provide some examples of each category. This will help Llama 2 understand the task and the possible outputs. We can use the following format for our prompt:
A news article can be classified as one of the following categories: World, Sports, Business, Sci/Tech. Examples: - World: "UN chief urges action on climate change as report warns of 'catastrophe'" - Sports: "Ronaldo scores twice in Manchester United return" - Business: "Apple delays plan to scan iPhones for child abuse images" - Sci/Tech: "SpaceX launches first all-civilian crew into orbit" Based on these categories, classify this news article:
We can use any examples that are relevant and representative of each category, as long as they are not too long or too short. We can also use more than one example per category if we want to provide more information to Llama 2.
Step 3: Format the input and output texts
The next step is to format the input and output texts that we want to provide to and receive from Llama 2. For the input text, we will use a placeholder for the news article that we want to classify, such as:
"Tesla to open Supercharger network to other EVs later this year"
For the output text, we will use a placeholder for the category that we expect Llama 2 to assign to the input text, such as:
We can also use brackets or tags to indicate the input and output texts, such as:
[INPUT] "Tesla to open Supercharger network to other EVs later this year" [OUTPUT] "Business" [/OUTPUT] [/INPUT]
This can help Llama 2 distinguish between the input and output texts and the rest of the prompt. However, this is optional and may not always improve the results.
Step 4: Test and evaluate the prompt
The final step is to test and evaluate the prompt using Llama 2. We can use any platform or tool that supports Llama 2, such as Meta’s website, Hugging Face’s website, or Replicate’s website. We can also use our own code or script to run Llama 2 locally or on a cloud service.
To test the prompt, we can provide different news articles as input texts and see what categories Llama 2 assigns to them as output texts. We can also compare the results with the ground truth labels from the AG News dataset or our own judgment. To evaluate the prompt, we can use different metrics such as accuracy, precision, recall, or F1-score, depending on our needs and preferences.
Here are some examples of testing and evaluating the prompt using Replicate’s website:
!Screenshot of Replicate’s website showing Llama 2 with 7B parameters and base variant using the prompt for text classification on AG News dataset
As you can see, Llama 2 correctly classified all four news articles according to their categories. However, this does not mean that Llama 2 is perfect or infallible. There may be cases where Llama 2 fails or makes mistakes, especially if the input text is ambiguous, noisy, or out of domain. Therefore, it is important to test and evaluate the prompt on a large and diverse set of inputs and outputs, and to monitor and improve the prompt over time.
Frequently Asked Questions (FAQs)
Here are some frequently asked questions about prompting Llama 2 for text classification:
Question: How many examples do I need to provide for each category?
Answer: There is no definitive answer to this question, as it may depend on various factors such as the task, the domain, the input format, the output format, the LLM size, and the LLM variant. However, a general rule of thumb is to provide at least one example per category, and more if possible. The more examples you provide, the more information you give to LLMs about what you expect from them.
Question: How long should the input and output texts be?
Answer: Again, there is no definitive answer to this question, as it may depend on various factors such as the task, the domain, the input format, the output format, the LLM size, and the LLM variant. However, a general rule of thumb is to keep the input and output texts as short and concise as possible, without losing essential information. The shorter the input and output texts are, the less memory and computation they require from LLMs.
Question: How can I improve the performance and accuracy of my text classifier?
Answer: There are several ways to improve the performance and accuracy of your text classifier using Llama 2. Some of them are:
- Experiment with different prompts and formats. You can try different ways of phrasing the instructions, examples, input, and output texts, and see how they affect the results. You can also use different formats such as brackets, tags, bullet points, or tables, and see how they affect the readability and clarity of the prompt.
- Use more examples and categories. You can provide more examples for each category to give more information and diversity to Llama 2. You can also use more categories if your task requires finer-grained classification. However, be careful not to overload Llama 2 with too many examples or categories, as this may reduce its performance or accuracy.
- Use a larger Llama 2 size or variant. You can use a larger Llama 2 size or variant if you have enough resources and need higher accuracy or complexity. For example, you can use the 13B or 70B parameters size, or the chat variant, if you want to handle longer texts, more categories, or more conversational inputs or outputs.
- Fine-tune Llama 2 on your specific dataset or domain. You can fine-tune Llama 2 on your specific dataset or domain if you have enough data and resources and want to customize Llama 2 for your task. This can improve the performance and accuracy of Llama 2 by adapting it to your data distribution and vocabulary. However, this may also introduce some issues such as data privacy, data scarcity, or data bias.
Question: What are the advantages and disadvantages of using Llama 2 for text classification?
Answer: Some of the advantages of using Llama 2 for text classification are:
- It is easy and fast to use. You do not need to collect, preprocess, or label a large amount of data, or train a complex model from scratch. You only need to design a simple and effective prompt and provide it to Llama 2.
- It is flexible and versatile. You can use Llama 2 for any text classification task, regardless of the domain, language, input format, or output format. You can also adjust the prompt according to your needs and preferences.
- It is powerful and accurate. Llama 2 can leverage its general knowledge and language skills to perform text classification tasks with high accuracy and fluency. It can also handle longer texts and more categories than most other LLMs.
Some of the disadvantages of using Llama 2 for text classification are:
- It is not perfect or infallible. Llama 2 may fail or make mistakes in some cases, especially if the input text is ambiguous, noisy, or out of domain. It may also produce inconsistent or unreliable results depending on the prompt, the size, or the variant.
- It is not transparent or explainable. Llama 2 does not provide any explanation or justification for its output texts. It may also produce output texts that are not aligned with human values or expectations, such as biased, offensive, or misleading texts.
- It is not free or cheap. Llama 2 requires a lot of computation and memory resources to run, which may incur high costs or limitations. It may also require a license or permission to use depending on the platform or tool.
In this blog post, we have shown you how to use Llama 2 for text classification tasks using a simple and effective technique called prompt engineering. We have explained what prompt engineering is, how to design a prompt for text classification, how to test and evaluate a prompt using Llama 2, and what are some tips and tricks to improve the performance and accuracy of your text classifier.
Disclaimer: This blog post is for educational purposes only and does not constitute professional advice. The results and opinions expressed in this blog post are based on our own experiments and observations using Llama 2 and may not reflect the official views or policies of Meta AI or any other organization. We do not guarantee the accuracy, completeness, reliability, or suitability of any information or output provided in this blog post. We are not responsible for any errors, omissions, damages, losses, liabilities, costs, expenses, claims, actions, suits, judgments, or consequences arising from or related to the use of Llama 2 or this blog post.