Overview of Prompt Engineering
BERT, described in “BERT Overview, Algorithm and Examples of Implementation,” has 350 million parameters and 3 billion words in its corpus, while GPT2, a development of GPT, described in “GPT Overview, Algorithm and Examples of Implementation,” has 1.5 billion parameters and 10-20 billion words in its corpus. GPT3, a more recent model, has 175 billion parameters and 300 billion corpora, a significant increase in the number of models. Such a large scale has led to a rapid improvement in performance,
Many tasks that were previously thought to be unsolvable without separate fine tuning can now be solved by the simple method of entering text, called a prompt, into the model and predicting the subsequent text.
Prompt Engineering” refers to techniques and methods used in the development of natural language processing and machine learning models to devise given textual prompts (instructions) to elicit the best response for a particular task or purpose. This is a particularly useful approach when using large-scale language models such as OpenAI’s GPT (Generative Pre-trained Transformer) using GPT model described in “Overview of GPT and examples of algorithms and implementations“.
The basic idea behind prompt engineering is to obtain better results by providing appropriate questions or instructions to the model. The prompts serve as inputs to the model, and their selection and expression affect the outputs of the model.
The following are general techniques and considerations for prompt engineering
1 Concreteness and Clarity: Prompts should be designed to contain specific and clear instructions; glib or abstract instructions will make it difficult for the model to produce the desired results.
2. understanding and adapting models: understand what types of prompts a particular model is good at and design prompts in a format appropriate for that model.
3. reinforcement of model weaknesses: If a model is prone to generating incorrect information on a particular topic or in a particular context, prompts should be devised to reinforce these weaknesses.
4. iterative experimentation: explore, through active experimentation and trial and error, what prompts elicit the desired results. Tailoring prompts is an iterative process, and it is important to be open to feedback from real-world use cases.
5. leveraging domain-specific knowledge: In certain domains, it is helpful to incorporate expertise in that domain into the prompts.
6. ethical considerations: In designing prompts, care should be taken to avoid inappropriate content or bias from an ethical perspective.
Types of Prompt Engineering
The type of prompt engineering depends on the context and purpose for which it is applied. The following describes some common types of prompt engineering.
1. fine-tuning prompt: Used to fine-tune an existing model for a specific task. Fine-tuning prompts are designed to provide specific inputs to the model and elicit specific outputs; for example, in the case of an emotion analysis task, a prompt such as “Please determine the emotion of this sentence” would be used.
2. bias correction prompts: If the model is subject to bias, prompts designed to correct for bias are used. This is a technique to address issues of fairness and bias, for example, introducing prompts to correct for gender and race biases.
3. prompts for generative tasks: prompts are designed to generate sentences with specific content and formatting in text generation tasks. This would be, for example, in the case of interactive text generation, prompts such as “two characters should talk to each other”.
4. Context-controlled prompts: A prompt is given to the model with a specific context or information to elicit a generation result that takes it into account, e.g., context control is used to generate the next sentence by considering the content of the previous sentence. The case where one example is given is called one-shot learning, the case where multiple examples are given is called few-shot learning, and the case where no examples are given at all is called zero-shot learning. Contextual learning is also considered to be a type of meta-learning as described in “Overview and Examples of Meta-Learners that can be Used for Few-shot/Zero-shot Learning” and “Overview of causal inference using Meta-Learners and examples of algorithms and implementations” because the method of learning itself is learned.
5. interactive prompts: Prompts to encourage interactive interaction with the model. This allows the model to adjust its output as the dialogue progresses.
6. model comprehension prompts: Prompts that help the model understand a particular concept or domain. This improves the model’s ability to adequately answer questions about a particular topic.
7. chain-of-thought inference prompts: One task that large-scale language models are not good at is multi-step inference, which requires inference in other steps. This can be seen, for example, in the following sentence: “There were 23 apples in the room. If you use 20 apples for cooking and buy 6 more, how many apples will be left?” The problem is to reason in two steps: 23 – 20 = 3 apples, then buy more apples and 3 + 6 = 9 apples remain. In order to apply this method to such a case, an example of the inference process is given, or a string such as “Let’s think step by step” is added to the end of the prompt to encourage the generation of the inference process (zero-shot chain-of-thought inference).
See the “Prompt Engineering Guide” for other prompt engineering techniques.
Libraries and platforms used for prompt engineering
In prompt engineering, especially when using large language models, libraries and platforms are used to manipulate those models and design prompts. The following describes several libraries and platforms used in prompt engineering.
OpenAI’s GPT-3 is a widely used tool for prompt engineering; OpenAI provides an API for using GPT-3, available directly through the OpenAI GPT-3 Playground. See also “Overview of ChatGPT and LangChain and its use” for more information on using OpenAI’s API.
2. Hugging Face :
Hugging Face provides a library and platform for natural language processing models; the Transformers library provides access to a variety of pre-trained models (e.g., GPT-2, GPT-3, BERT, etc.), which can be used for prompt engineering. For more information on Hugging Face, see “Overview of Automatic Sentence Generation with Hugging Face.
3. OpenAI Codex (GitHub Copilot):
The OpenAI Codex, also known as GitHub Copilot, is based on the GPT model and allows for prompt engineering in programming. This is especially useful in code generation, where tools for code completion and generation prompt design are available. See also “Overview of the OpenAI Codex and its use” for more information.
4. DeepPrompt:
DeepPrompt is a platform dedicated to prompt engineering that makes it easy to design prompts for models in different tasks and domains. For more information, see “DeepPrompt Overview and Usage.
5. LangChain:
LangChain is a library that helps develop applications using language models and provides a platform on which you can build various applications using ChatGPT and other generative models. “Overview of ChatGPT and LangChain and its use” for more information on LangChain.
Application Examples of Prompt Engineering
The following are some examples of prompt engineering applications.
1. question-and-answer system:
Medical consultation: A medical professional or patient asks questions about symptoms, and a model designs prompts to provide appropriate advice or diagnosis.
General knowledge question-and-answer: Users ask questions about any topic, and models design prompts to provide appropriate answers.
2. content generation:
Writing support: When generating articles or reports on a specific topic, design prompts so that the model generates sentences with a specific point of view and style.
Poetry and fiction generation: designing prompts so that the model generates a continuation based on a specific scene or theme in a poem or novel.
3. generating computer programs:
Code completion: design prompts so that when a programmer writes code based on a specific function or algorithm, the model generates the appropriate code fragments.
Game AI generation: designing prompts for game developers to generate AI for games with specific behaviors and tasks.
4. creative content generation:
Image generation: design prompts for image processing tasks such as image caption generation, changing image styles, generating specific parts of an image, etc.
Music and melody generation: design prompts to generate music based on a specific genre or mood.
These are only a few examples; prompt engineering can be applied in a variety of domains, and by designing appropriate prompts, models can generate outputs appropriate for specific tasks.
Examples of Prompt Engineering Implementations
As an example of prompt engineering implementation, we show a prompt design for GPT-3 using Hugging Face’s Transformers library. This example describes how to use Hugging Face Transformers to provide prompts to GPT-3 and elicit the desired form of response.
First, install the Transformers library.
pip install transformersThe following Python code is then used to provide prompts for GPT-3.
from transformers import GPT2LMHeadModel, GPT2Tokenizer
def generate_prompt_response(prompt):
# GPT-3 model and tokenizer loading
model = GPT2LMHeadModel.from_pretrained("gpt2")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
# Tokenize prompts
input_ids = tokenizer.encode(prompt, return_tensors="pt")
# Provide prompts to GPT-3 for generated results
output = model.generate(input_ids, max_length=100, num_beams=5, no_repeat_ngram_size=2, top_k=50, top_p=0.95, temperature=0.7)
# Decode generated tokens to text
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
return generated_text
# Example: design a prompt, provide it to GPT-3, and get the generated text
prompt_example = "Translate the following English text to French: 'Hello, how are you?'"
response_example = generate_prompt_response(prompt_example)
print("Input Prompt:")
print(prompt_example)
print("nGenerated Response:")
print(response_example)In this example, a prompt is designed to translate English text into French and provided to GPT-3 to obtain the generated response. The key to prompt engineering lies in the specificity and clarity of the prompt, the understanding and adaptation of the model, as well as appropriate parameter tuning, through tweaking and experimentation with the prompt to achieve the desired form of result.
Prompt Engineering’s Challenges and Measures to Address Them
The following is a discussion of prompt engineering issues and how to address them.
1. bias and ethics issues:
Challenge: Bias and bias can occur in the design of training data and prompts. This may result in inappropriate bias reflected in the output of the model.
Solution: It is important to choose words carefully in the design of prompts and to create prompts that are fair and ethical. It is also helpful to incorporate diverse perspectives in evaluating the results generated.
2. responding to unknown prompts:
Challenge: The model is based on training data and may generate inappropriate responses to unknown prompts.
Solution: Increasing prompt variation and diversifying training data and prompts to accommodate unknown situations would be a useful approach.
3. difficulty in finding appropriate prompts:
Challenge: It can be difficult to find out which prompts are optimal, especially in large models.
Solution: Build a process for finding optimal prompts through iterative experimentation and trial and error. It will also be useful to use user feedback and model performance evaluation to adjust the prompts.
4. uncertainty in the generated results:
Challenge: Model-generated results may contain uncertainty due to varying degrees of confidence.
Solution: Implement means of communicating uncertainty to users so that they can consider it in appropriate situations. It will also be important to obtain multiple samples through probabilistic generation and consider the variations.
5. prompt dependencies:
Challenge: Fine-tuning of prompts can have a significant impact on model performance, and over-dependence on specific prompts can be a challenge.
Solution: Try multiple prompts and devise ways to ensure that the model’s performance is not strongly dependent on the prompt. In addition, it will be important to pay attention to the training data and design of the model to ensure that model improvement is not dependent on the prompt.
Reference Information and Reference Books
For details on automatic generation by machine learning, see “Automatic Generation by Machine Learning.
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