With the advancement of natural language processing into large language models, it has become difficult to understand why models behave the way they do - for example, how the model behaves if you give it specific information or the difference in outputs from the ways we feed information. This has given birth to a new kind of career path called Prompt Engineering.
Prompt engineering is like giving special instructions to the model so it knows exactly what you want. It's a way to make the program smarter and more useful. For example, if you want the program to write a story about a superhero, you'd give it a special 'prompt' that says, 'Write a story about a superhero saving the day.' This way, the program knows the topic and can create a story that fits your request.
Not just that, prompt engineering is also used to improve how these smart programs work in the first place. It's like teaching them to get better at understanding what people want. It's a bit like tuning a guitar so it plays better music. When you refine the input or prompt, you make sure the program creates even better and more accurate stuff, making it a super helpful tool for all sorts of exciting things!
Importance of Prompts in Natural Language Tasks
You might be wondering here: why is it so important to use prompt engineering in the first place? Why can’t we just prompt randomly, because AI will eventually understand what we want? So, here are some reasons why incorporating prompt engineering can enhance your experience:
1. Adaptability to industry-specific requirements: You can make sure with the prompt which specific industry you want to target.
Eg. ‘Write a short report on the effect of rising temperatures on the Agriculture Industry.’
2. Enhanced Accuracy: You can enhance the quality of your output by giving more information in prompts so the model can learn from them.
Eg. ‘Refer to Indian history and answer: When was the Declaration of Independence signed?’
3. Ethical Considerations: By incorporating prompt engineering, one can overcome the potential biases and harmful consequences of LLM models.
Eg. ‘Write an essay discussing the ethical considerations surrounding animal testing in medical research, including both its potential benefits and concerns for animal welfare and moral implications.’
There are other benefits which we will discuss further in the blog.
Understanding Zero-Shot Learning with Prompts
It is a basic prompting technique giving instructions to the model to get the output. One can directly give specific prompts and generate responses from the model for which it was not specifically trained. The model can understand the context and patterns by utilising the information it gained during training.
During the training of large language models, they learn from a vast amount of data sources. As a result, the model becomes capable of learning even from a short sentence without any task-specific examples or understanding context and patterns.
For example,
Prompt: ‘Extract the sentiment from the following sentence.’
The model can produce the output without prior training on sentiment analysis tasks.
Advantages of Zero-Shot Learning
1. No training is required to perform zero-shot prompting, which makes it adaptable to new scenarios.
2. Since no training data is required, there’s no need to store data anywhere.
3. Generalised model.
4. Computationally efficient.
Limitations of Zero-Shot Learning
1. Lower performance than task-specific models.
2. Potential for erroneous or uncertain information.
3. Vocabulary mismatch from the data on which the model is trained upon.
4. No detailed knowledge of the specific task.
Prompt Engineering for Few-Shot Learning
Prompt engineering is a way of teaching a model to perform a specific task using a few examples. These examples show the model what the correct inputs and outputs should look like for that task. The purpose of this method is to explain the intent of the model and describe to the model how a job needs to be performed in the form of examples.
By seeing these ‘good’ examples, the model learns to understand what people want and the criteria for providing the right answers. This method often leads to better results compared to a scenario where the model has to answer with zero examples.
Challenges and Tips to Overcome
Few-shot classification faces challenges due to biases in large language models (LLM), like:
● Majority Label Bias: when the distribution of labels across examples is unbalanced.
● Recency Bias: repetition of labels at the end.
● Common Token Bias: when the reproduction of a common token is more often prioritised.
To address these biases, a method is proposed to calibrate label probabilities for N/A inputs. To select suitable examples, using NN clustering in the embedding space helps find semantically similar ones. Another approach is a graph-based method, which involves constructing a directed graph based on cosine similarity between samples, promoting diversity in the selection.
For ordering, it is advised to keep the selection diverse, relevant, and random to avoid biases. Model size and the number of training examples don't necessarily reduce variance in different permutations. Choose orders that prevent extreme imbalances or overconfidence in predictions when the validation set is limited.
Advantages of Few-Shot Learning
1. Better performance, as the model can understand the intent of the prompt.
2. Increased generalisation to a specific task without fine-tuning.
3. Faster than fine-tuning to train the model to understand the intent.
Limitations of Few-Shot Learning
1. Overfitting.
2. Potential bias towards the examples provided.
For Example:
Normally, you can write the prompt as: ‘Convert the following sentence into French.’
Using Few-Shot Prompting, you can write the prompt as:
‘Convert the sentence to French: Hello, how are you?
bonjour comment ca va
Convert the sentence to French: Thank you.
Merci
Convert the sentence to French: Where are you going?’
Chain of Thought: Sequencing Prompts for Coherent Text Generation
Chain-of-thought (CoT) prompting, introduced by Wei et al. in 2022, involves generating short sentences that explain the reasoning steps one by one, leading to the final answer. These are called reasoning chains or rationales. CoT works best for complex reasoning tasks when using large models with many parameters. However, for simple tasks, the benefits of CoT are only marginal.
There are two main types of CoT prompts:
1. Few-Shot CoT: This involves giving the model a few demonstrations, each containing well-written reasoning chains either created by humans or generated by the model itself.
2. Zero-Shot CoT: This involves using statements like ‘Let’s think step by step’ to encourage the model to go over the solution step by step.
For Example:
Normally, you can write a prompt as ‘Write an article on the French Revolution.’
Using Chain of Thought prompting, you can write the prompt as:
‘Who was involved in the French Revolution and what were the main events? What was the cause of the French Revolution? Describe the convening of the Estates-General and its significance in the early stages of the Revolution. Discuss the rise of Jacobins and the Reign of Terror.’
Self-Consistency
This method involves asking multiple queries to the model to guide its later responses so the model can learn. We can take advantage of multiple prompts before the final prompt and make the model learn by itself, to add the information as a context to the model. This helps in fine-tuning the model on specific styles by iteratively correcting and improving the results, thereby encouraging coherence.
The caveat is that this technique has a higher risk of getting biased outputs if the initial prompts are incorrect, so it requires careful handling
Example:
Prompt: ‘You are an AI language model writing a story. Once upon a time…’
Model's Response:
There was a brave knight who ventured into the enchanted forest.
New Prompt: ‘The knight's name was Sir Arthur, and he carried a legendary sword called Excalibur. He decided to explore deeper into the forest.’
Model's Improved Response:
As Sir Arthur ventured deeper into the forest, he encountered mythical creatures and magical challenges that tested his bravery and skills.
General Knowledge Prompting
General knowledge prompting involves providing the model with external factual information or context through prompts to guide its responses. The prompts typically include explicit information on a topic or domain.
Advantages
- Helps the model generate more accurate and factual information, especially in domains it may not be familiar with.
- Enables the model to answer questions or provide explanations that require external knowledge.
- Can improve the model's reliability in providing informative responses.
Disadvantages
- May result in overly verbose or redundant responses as the model relies heavily on provided information.
- Can limit the model's ability to generate creative or imaginative content.
Example:
Prompt: ‘Define photosynthesis.’
Model's Response:
Photosynthesis is the process by which green plants and some other organisms use sunlight to synthesize foods with the help of chlorophyll, converting carbon dioxide and water into glucose and oxygen.
Tree of Thoughts
Tree of thoughts involves providing the model with a structured prompt hierarchy or a sequence of related questions. The model's responses to earlier prompts inform the subsequent ones, leading to a coherent and in-depth generation.
Advantages
- Facilitates the generation of more detailed and organized responses.
- Enables the model to explore different aspects of a topic in a structured manner.
- Helps maintain context and coherence throughout the generation process.
Disadvantages
- Requires careful planning and design of the prompt sequence.
- Longer sequences may lead to potential errors or misunderstandings in the model's responses.
Example:
Prompt:
‘1. What is your favorite color?
2. Why do you like that color?
3. Can you recall any fond memories associated with that color?’
Model's Response:
1. My favorite color is blue.
2. I like blue because it reminds me of the calm ocean and the clear sky.
3. One fond memory is when I went on a beach vacation with my family, and the vibrant blue sea made the whole experience magical.
Retrieval Augmented Generation
Retrieval augmented generation involves incorporating information retrieved from external sources or databases into the model's prompts to enhance the quality and accuracy of its responses.
Advantages
- Enriches the model's knowledge and ability to provide well-informed responses.
- Reduces the risk of generating incorrect or misleading information.
- Supports the model in handling complex or specialized queries effectively.
Disadvantages
- May add computational overhead due to the retrieval process.
- The quality of the retrieved information can impact the overall performance of the model.
Example:
Prompt: ‘In 2019, which country hosted the FIFA Women's World Cup?’
Retrieval: The model retrieves information from a sports database that France hosted the FIFA Women's World Cup in 2019.
Model's Response: The FIFA Women's World Cup in 2019 was hosted by France.
Automatic Reasoning and Tool Use
This process involves leveraging computational methods and tools to automatically generate effective prompts for natural language processing tasks. This technique utilizes algorithms and machine learning models to analyze the task requirements, input data, and target outputs to generate prompts that aid the model in solving the task accurately.
ART pulls examples of similar tasks from a task library to allow a few-shot breakdown and tool usage for further work. These examples use a flexible yet structured query language that makes it simple to read intermediate stages, pause creation to use external tools, and restart it once the output of those tools has been included
Advantages
- Reduces manual effort and human biases.
- It enables prompt engineers to explore a wide range of prompt variations quickly, leading to improved model performance.
- Facilitates the adaptation of prompt engineering techniques to various domains and tasks.
Disadvantages
- Lacks creativity or the failure to capture specific nuances of the task.
- The quality of prompts heavily depends on the underlying algorithms, which may not always produce optimal results.
Example:
In sentiment analysis, an automatic prompt generation tool analyzes a dataset of customer reviews and their corresponding sentiments. Based on this analysis, the tool generates prompts such as, ‘Is the following statement positive/negative/neutral?’ or, ‘How do you feel about the following statement?’, which can be used to train a sentiment analysis model.
Automatic Prompt Engineer
The automatic prompt engineer is an AI-based system designed to autonomously devise appropriate prompts for a given natural language processing task. This technique incorporates pre-trained language models and reinforcement learning methods to iteratively generate and evaluate prompts based on task performance feedback.
Advantages
- Reduces the need for manual intervention
- Dynamically adapts prompts during training, leading to continuous improvement in model performance.
- Effectively handles complex tasks with diverse input types.
Disadvantages
- Developing a reliable automatic prompt engineer requires significant computational resources and training data. The approach may also encounter challenges in certain low-resource or highly specialized domains where pre-trained models might not be optimal.
Example:
For question-answering tasks, the automatic prompt engineer starts with generic prompts and gradually refines them through reinforcement learning. During each iteration, it generates new prompts, evaluates the model's performance, and uses the feedback to modify and improve the prompts until the model achieves high accuracy.
Active Prompt
Active prompting in prompt engineering involves interactive human involvement during prompt generation. It requires human annotators to iteratively design, evaluate, and refine prompts based on the model's responses to enhance its performance.
Advantages
- Allows prompt engineers to inject domain expertise and creativity into the process, tailoring prompts to specific task requirements.
- Enables prompt engineers to adapt to the model's weaknesses and improve overall performance effectively.
Disadvantages
- Time-consuming and resource-intensive, as it relies on human input and iterative model training.
- Subjectivity of human judgments may introduce biases in prompt design.
Example:
In machine translation, the active prompt technique involves human annotators providing translations of sample sentences. The model uses these translations to generate prompt variations for further evaluation. The annotators iteratively refine prompts until the model produces accurate translations for various inputs.
Directional Stimulus Prompting
Directional Stimulus Prompting is a technique in prompt engineering that involves providing specific cues or directions to a language model to elicit desired responses. By incorporating explicit instructions within the prompts, the model can be guided to focus on particular aspects of the input or generate responses with a predetermined tone or sentiment. This approach is particularly useful when fine-tuning a language model for sentiment analysis, language translation, or generating text with a specific writing style.
Advantages
- Ability to ensure more consistent and controlled outputs.
- Reduces the chances of generating inappropriate or undesirable content.
Disadvantages
- Limited ability to generalize effectively.
- Suboptimal performance on tasks requiring more creative or contextually nuanced responses.
Example:
For sentiment analysis, a directional stimulus prompt might be: ‘Analyze the following review and provide a positive sentiment about the product.’
By incorporating this direction, the language model can focus on generating responses that emphasize positive aspects of the product, which can be valuable for companies seeking to understand customer feedback.
ReAct (Reinforced Active Learning with Contrastive Text)
ReAct is a prompt engineering technique that leverages reinforcement learning principles to enhance the performance of language models in few-shot or zero-shot scenarios. It involves using contrastive text, wherein multiple alternative completions of the same prompt are presented to the model, and it is rewarded based on selecting the most accurate or contextually appropriate response. This approach enables the model to learn from its mistakes, encouraging more robust and adaptive behavior.
Advantages
- Handles diverse prompts and generates coherent responses in challenging settings.
- Reduces errors or hallucinations.
Disadvantages
- Computationally intensive.
- Requires careful tuning to strike the right balance between exploration and exploitation.
Example:
In a dialogue system, ReAct can be applied by presenting the language model with multiple possible responses to a user query. The model is then rewarded for selecting the most contextually relevant and informative reply. Through this reinforcement mechanism, the model learns to produce more accurate and contextually appropriate responses during real-world interactions.
Multimodal CoT (Chain of Thought)
Multimodal CoT, also known as Chain of Thought, is an advanced prompt engineering technique that involves sequencing prompts to guide the generation of coherent and contextually connected text. This approach allows the language model to maintain a consistent chain of thought throughout the generated text, making it more suitable for tasks like story generation, summarization, and question answering. By linking prompts together, the model can ensure that each subsequent response is informed by the preceding context, leading to more fluent and contextually accurate outputs. However, a challenge of using Multimodal CoT is finding the right balance between maintaining coherence and avoiding repetition or monotony in the generated text.
Example:
In a story generation task, Multimodal CoT can be applied by presenting the language model with a sequence of prompts: ‘You are a detective investigating a mysterious murder in a quaint town. Describe the crime scene. Interview a witness. Uncover a crucial clue. Solve the case.’
By chaining these prompts, the language model can craft a coherent and engaging detective story, with each response building upon the previous one to create a compelling narrative.
Graph Prompting
Graph Prompting is an advanced technique used in prompt engineering to leverage structured information, such as knowledge graphs, to enhance the performance of natural language processing models. Instead of using traditional text-based prompts, graph prompting involves constructing prompts in the form of graph structures, representing entities and their relationships, to guide the language model's understanding and generation capabilities.
Advantages
1. Enhanced Semantics: Graph prompts capture rich semantic relationships between entities, enabling the model to access a wealth of knowledge during inference.
2. Contextual Embeddings: By representing information in a graph format, the model can better understand the contextual significance of entities within the prompt.
3. Scalability: Knowledge graphs offer a scalable way to organize and represent vast amounts of information, making it feasible to handle complex tasks and domains.
Disadvantages
1. Complexity: Building and maintaining accurate knowledge graphs can be a labor-intensive and challenging task.
2. Data Sparsity: In certain domains, the knowledge graph might lack comprehensive information, leading to potential gaps in the model's understanding.
3. Inference Overhead: Processing graph-based prompts can require additional computational resources, impacting inference speed.
Example:
Consider an information retrieval task where the goal is to generate relevant answers to user queries from a knowledge base. Instead of using a simple text-based prompt like ‘Generate an answer for the query. “What is the capital of France?”,’ the graph prompting involves constructing a knowledge graph with entities like ‘France’ and ‘Capital’ connected by a ‘Has Capital’ relationship.
Prompt:
‘Node 1: Entity - France
Edge: Has Capital
Node 2: Entity - Capital’
The language model, when presented with this graph prompt, can infer the relationship between ‘France’ and ‘Capital’ and generate the answer ‘Paris’ based on the information stored in the knowledge base. By incorporating structured information, the model gains a deeper understanding of the query and produces more accurate responses, showcasing the effectiveness of graph prompting in information retrieval tasks.
Conclusion
Prompt engineering is a crucial step when it comes to getting the right answers from a large language model. There are a wide range of options you can choose from depending on your need – and there might be a lot more coming with more advancements in technology. Each technique offers unique benefits and challenges, enabling AI language models to be more adaptive, accurate, and contextually aware in generating responses.
In conclusion, Prompt Engineering is still a very new domain in the field of Artificial Intelligence and has vast potential for development and improvement. It is an upcoming field which will grow and provide new job opportunities.
At E2E, our clients are experimenting with prompt engineering to generate some fabulous responses. Try it out for yourself.
