Testing Prompt Engineering Methods for Knowledge Extraction from Text

The capabilities of Large Language Models (LLMs,) such as Mistral 7B, Llama 3, GPT-4, present a significant opportunity for knowledge extraction (KE) from text. However, LLMs' context-sensitivity can hinder obtaining precise and task-aligned outcomes, thereby requiring prompt engineering. This study explores the efficacy of five prompt methods with different task demonstration strategies across 17 different prompt templates, utilizing a relation extraction dataset (RED-FM) with the aforementioned LLMs. To facilitate evaluation, we introduce a novel framework grounded in Wikidata's ontology. The findings demonstrate that LLMs are capable of extracting a diverse array of facts from text. Notably, incorporating a simple instruction accompanied by a task demonstration—comprising three examples selected via a retrieval mechanism—significantly enhances performance across Mistral 7B, Llama 3, and GPT-4. The effectiveness of reasoning-oriented prompting methods such as Chain-of-Thought, Reasoning and Acting, while improved with task demonstrations, does not surpass alternative methods. This suggests that framing extraction as a reasoning task may not be necessary for KE. Notably, task demonstrations leveraging examples selected via retrieval mechanisms facilitate effective knowledge extraction across all tested prompting strategies and LLMs.