In the context of Large Language Models (LLMs), keyword extraction, query expansion, and paraphrasing techniques enhance the effectiveness of more complex prompts. Keyword extraction involves identifying and extracting key terms from prompts to provide clearer instructions, such as “List the causes of climate change.” Query expansion broadens the scope of prompts by adding related terms or synonyms, for example, “Discuss the impact of global warming and climate change on biodiversity.” Paraphrasing rephrases prompts to enhance clarity or convey the same meaning in a different manner, like “Explain how deforestation contributes to ecological imbalance.” These techniques help refine and optimize prompts, enabling users to convey their intent more precisely and obtain more accurate and relevant responses from the LLM.
Keyword Extraction: Keyword extraction involves identifying and extracting important keywords or key phrases from a given text. In the context of constructing prompts for an AI keyword extraction can be helpful in determining the main focus or topic of a question or query. By identifying the essential keywords, the AI can better understand the user’s intent and provide more relevant and accurate responses. Keyword extraction helps in narrowing down the scope of the conversation and guiding the AI’s attention to the most critical aspects of the query.
Query Expansion: Query expansion is the process of broadening or enhancing a user’s query to improve the retrieval of relevant information. It involves adding related terms or synonyms to the original query to capture a wider range of relevant results. In the context of constructing prompts for an AI, query expansion can be used to generate more comprehensive and diverse prompts by incorporating alternative terms or phrases that convey similar meaning. This helps in exploring different aspects of a topic and facilitates a more extensive exploration of ideas and information.
Paraphrasing: Paraphrasing involves restating a sentence or a passage using different words or sentence structures while preserving the original meaning. In the context of constructing prompts for an AI, paraphrasing can be employed to present a question or query in multiple ways. By providing paraphrased versions of a prompt, the AI can better understand user input that might be expressed differently. Paraphrasing also helps in overcoming language variations, preferences, or limitations by offering alternative phrasing to elicit the desired response.
In the above examples gardening was used to highlight the differences in the composition of suitable prompts. Another example could be the French Revolution:
Prompt: “What are the most important facts about the history of the French Revolution?”
Query Expansion Example:
Prompt: “What were the major causes and effects of the French Revolution?”
Prompt: “What led to the French Revolution and what were the consequences?”
Prompt: “What do we know about the Reign of Terror during the French Revolution?”
Query Expansion Example:
Prompt: “What political, social and economic conditions led to the Reign of Terror during the French Revolution?”
Prompt: “Can you provide information on the historical period known as the Reign of Terror in the context of the French Revolution? Specifically, I’m interested in understanding the factors and circumstances, whether political, social, or economic, that contributed to the occurrence of the Reign of Terror.”