How to Use Prompt Engineering for Knowledge Extraction and Reasoning with Pre-trained Language Models

Knowledge extraction and reasoning are two important tasks in natural language processing (NLP) that aim to extract structured information from unstructured text and infer new facts from existing knowledge. However, these tasks are challenging due to the complexity and diversity of natural language, as well as the lack of large-scale annotated data.

Prompt Engineering for Knowledge Extraction and Reasoning

One promising approach to overcome these challenges is prompt engineering, which is the process of designing natural language queries or instructions that can elicit the desired responses from pre-trained language models (PLMs) such as BERT or GPT-3. Prompt engineering leverages the implicit knowledge and reasoning abilities of PLMs without requiring fine-tuning or additional supervision.?In this blog post, we will introduce the concept and benefits of prompt engineering, discuss some of the best practices and challenges of prompt engineering, and provide some examples of prompt engineering for knowledge extraction and reasoning tasks.?

What is Prompt Engineering??

Prompt engineering is a form of few-shot or zero-shot learning that uses natural language as a way to communicate with PLMs and guide them to perform specific tasks. A prompt consists of three main components: a context, a query, and a response format.?

1. The context provides some background information or examples that help the PLM understand the task and the domain.

2. The query specifies the question or instruction that the PLM needs to answer or follow.

3. The response format indicates how the PLM should format its output, such as using a separator, a label, or a template.

For example, suppose we want to use a PLM to extract the names and occupations of famous people from a given text. A possible prompt for this task could be:?

Given a text, extract the names and occupations of famous people mentioned in it. Use a colon (:) to separate the name and the occupation, and use a comma (,) to separate different people. For example:?

Text: Albert Einstein was a physicist who developed the theory of relativity. He was awarded the Nobel Prize in Physics in 1921.

Output: Albert Einstein:physicist?

By providing this prompt to a PLM, I hope that it can generalize from the example and apply the same logic and format to other texts. Of course, not all prompts are equally effective, and finding the optimal prompt for a given task and model can be a trial-and-error process that requires some intuition, creativity, and experimentation.?

Why Prompt Engineering??

Prompt engineering has several advantages over other methods of using PLMs for downstream tasks, such as fine-tuning or task-specific architectures.?Prompt engineering is data-efficient, as it does not require large amounts of labeled data for each task. Instead, it relies on the natural language understanding and generation capabilities of PLMs that have been pre-trained on massive amounts of unlabeled text.

Prompt engineering is model-agnostic, as it does not depend on the specific architecture or parameters of PLMs. Instead, it uses natural language as a universal interface that can be understood by any PLM that supports text input and output.

Prompt engineering is flexible and interpretable, as it allows users to control and customize the behavior and output of PLMs using natural language. Instead of treating PLMs as black boxes, users can interact with them using prompts that are easy to write, read, and modify.

How to Do Prompt Engineering??

Prompt engineering is both an art and a science, as it requires both linguistic skills and empirical methods. There is no definitive answer to what makes a good prompt, but there are some general guidelines and best practices that can help users design effective prompts for their tasks.?

1. Understand your task and your model.?

Before writing a prompt, you should have a clear idea of what you want to achieve and what your model can do. You should also familiarize yourself with the input and output formats, the vocabulary, and the capabilities and limitations of your model.

2. Use natural and unambiguous language.?

Your prompt should be written in natural language that is easy for both humans and models to understand. You should avoid using jargon, slang, abbreviations, or complex syntax that could confuse your model. You should also make sure that your prompt is unambiguous and does not leave room for multiple interpretations or irrelevant responses.

3. Provide sufficient context and examples.?

Your prompt should provide enough context and examples that help your model grasp the task and the domain. You should include relevant information that could aid your model’s reasoning or retrieval processes, such as definitions, facts, rules, or scenarios. You should also provide positive and negative examples that illustrate the expected output and common errors or exceptions.?

4. Optimize your query and response format.?

Your prompt should have a query that is specific and direct enough to elicit the desired response from your model. You should avoid asking vague or open-ended questions that could lead to incomplete or inaccurate answers. You should also have a response format that is consistent and structured enough to facilitate your evaluation or application. You should use separators, labels, templates, or other markers that help you parse or extract information from your model’s output.?

5. Experiment and evaluate your prompt.?

Your prompt should be tested and evaluated using different texts or inputs that cover various cases and situations. You should check your model’s output for correctness, completeness, relevance, and coherence. You should also compare your prompt with alternative prompts that use different words, structures, or formats. You should analyze the results and identify the strengths and weaknesses of your prompt.?

Examples of Prompt Engineering?

To illustrate how prompt engineering works in practice, we will show some examples of prompts for knowledge extraction and reasoning tasks using GPT-3 as our PLM. Note that these prompts are not necessarily optimal or final solutions but rather demonstrations of possible approaches.?

Knowledge Extraction?

Knowledge extraction is the task of extracting structured information from unstructured text sources such as documents or web pages. For example:?

Named entity recognition—identifying and classifying entities such as persons, organizations, locations, dates, etc. in text.?

Relation extraction—identifying and classifying relations between entities in text, such as birthplace, spouse, occupation, etc.

Event extraction—identifying and classifying events and their participants, times, locations, etc. in text.

For these tasks, we can use prompts that provide definitions and examples of the target categories and use separators or labels to mark the extracted information in the output. For instance:?

Given a text, extract all the names of persons (PER), organizations (ORG), locations (LOC), and dates (DATE)mentioned in it. Use brackets [ ] to enclose each entity and its type, and use commas, to separate different entities.

For example:?

Text: Barack Obama was born on August 4,

1961, in Honolulu, Hawaii. He served as the 44th president of the United States from 2009 to 2017. He is married to Michelle Obama, a former lawyer and first lady.

Output: [Barack Obama:PER], [August 4,1961:DATE], [Honolulu:LOC], [Hawaii:LOC], [the United States:LOC], [2009:DATE], [2017:DATE], [Michelle Obama:PER], [a lawyer:ORG], [first lady:ORG]

Reasoning?

Reasoning is the task of inferring new facts or conclusions from existing knowledge or premises using logic or common sense. For example:?Natural language inference—determining whether a natural language hypothesis is true, false, or unknown given a natural language premise.

Commonsense reasoning—answering questions or completing sentences that require commonsense knowledge or understanding of everyday situations.?

Mathematical reasoning—solving mathematical problems or proving mathematical statements using symbols or natural language.?

For these tasks, we can use prompts that provide instructions or queries that specify the reasoning goal and use templates or formats that indicate the expected output. For instance:?

Given two natural language sentences, a premise and a hypothesis, determine whether the hypothesis is true (T), false (F), or unknown (U) based on the premise. Use one letter (T,F,U) as your answer.

For example:?

Premise: All cats are mammals.

Hypothesis: Some cats are reptiles.

Answer: F

Conclusion

Prompt engineering is a promising technique that allows users to harness the implicit knowledge and reasoning abilities of pre-trained language models for various downstream tasks without requiring fine-tuning or additional supervision. Prompt engineering involves designing natural language queries or instructions that can elicit the desired responses from pre-trained language models. Prompt engineering has several advantages over other methods, such as data efficiency, model agnosticism, flexibility, and interpretability.?

However, prompt engineering also poses some challenges, such as finding the optimal prompt, ensuring the quality and consistency of the output, and dealing with the ethical and social implications of using pre-trained language models.?

In this blog post, I have introduced the concept and benefits of prompt engineering, discussed some of the best practices and challenges of prompt engineering, and provided some examples of prompt engineering for knowledge extraction and reasoning tasks. I hope that this blog post has inspired you to explore the potential of prompt engineering for your own tasks and domains.

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