Large Language Models ↔ prompt engineering

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related 3.91 — strongly supporting 14 facts

Prompt engineering is a critical methodology used to guide, optimize, and enhance the reasoning and task-specific performance of Large Language Models, as evidenced by academic literature [1], [2] and practical applications like fact-checking [3], [4], entity extraction [5], and relation extraction [6]. Techniques such as Chain-of-Thought prompting are specifically employed to improve model reasoning and mitigate hallucinations [7], [8], [9].

Facts (14)

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KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org arXiv 2 facts

claimPrompt engineering techniques, including Chain of Thought (CoT), Tree of Thought (ToT), Graph of Thoughts (GoT), and ReAct (Reason and Act), have demonstrated significant improvements in the reasoning abilities and task-specific actions of Large Language Models.

LLM-Powered Knowledge Graphs for Enterprise Intelligence and ... arxiv.org arXiv 2 facts

claimThe relation extraction component utilizes Large Language Models (LLMs) with advanced prompt engineering, incorporating both contextual data from the Contextual Retrieval Module (CRM) and extracted entities as input to enhance the precision and relevance of relationship extraction.

claimThe entity extraction component improves precision and consistency by using Large Language Models (LLMs) with prompt engineering and contextual data retrieved from the Contextual Retrieval Module (CRM).

Integrating Knowledge Graphs into RAG-Based LLMs to Improve ... thesis.unipd.it Università degli Studi di Padova 2 facts

claimCustom prompt engineering strategies are necessary for fact-checking systems because different LLMs benefit from different types of contextual information provided by knowledge graphs.

claimEffective fact-checking performance requires custom prompt engineering strategies because different Large Language Models benefit from different types of contextual information.

Survey and analysis of hallucinations in large language models frontiersin.org Frontiers 2 facts

claimPrompt engineering, particularly Chain-of-Thought (CoT) prompting, reduces hallucination rates in large language models but is not universally effective.

claimPrompt engineering is not a universal solution for mitigating hallucinations in large language models, particularly for models with strong internal biases.

Combining large language models with enterprise knowledge graphs frontiersin.org Frontiers 2 facts

claimIn-context learning offers greater flexibility for adapting to the rapidly evolving field of Large Language Models (LLMs), though prompt engineering is time-consuming and requires methods that are not universally applicable across models, as reported by Zhao et al. (2024).

referenceRecent literature identifies two primary approaches to named entity recognition and relation extraction: creating large training sets with hand-curated or extensive automatic annotations to fine-tune large language models, or using precise natural language instructions to replace domain knowledge with prompt engineering.

Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org arXiv 1 fact

claimPrompt engineering techniques, including Chain-of-Thought (CoT) prompting, zero-shot prompting, and few-shot prompting, enable Large Language Models (LLMs) to reason and generalize across diverse tasks without requiring extensive retraining.

The construction and refined extraction techniques of knowledge ... nature.com Nature 1 fact

referenceChen, B. et al. published 'Unleashing the potential of prompt engineering in large Language models' in Patterns 6 (6), 101260 (2025).

A Survey on the Theory and Mechanism of Large Language Models arxiv.org arXiv 1 fact

referenceThe paper 'Unleashing the potential of prompt engineering in large language models: a comprehensive review' is an arXiv preprint, identified as arXiv:2310.14735.

Practices, opportunities and challenges in the fusion of knowledge ... frontiersin.org Frontiers 1 fact

claimPrompt engineering for Knowledge Graph (KG) completion involves designing input prompts to guide Large Language Models (LLMs) in inferring and filling missing parts of KGs, which enhances multi-hop link prediction and allows handling of unseen cues in zero-sample scenarios.