Event Plot Graph (EPG) to retrieve relevant event plot points

Sasaki et al. (2024): "Systematic literature review of prompt engineering patterns in software engineering." This work likely explores how structured patterns in prompts can be applied to software engineering tasks, which could include the organization and retrieval of event-related information.

Sahoo et al. (2024): "A systematic survey of prompt engineering in large language models: Techniques and applications." This survey would provide an overview of various prompt engineering techniques and their applications, potentially including methods for querying and extracting specific data points like event plot points from LLMs.

Sasson Lazovsky et al. (2024): "The art of creative inquiry—from question asking to prompt engineering." This suggests a focus on how the formulation of questions (prompts) can drive inquiry and discovery, which is relevant to retrieving specific event plot points.

Schmidt et al. (2024): "Towards a catalog of prompt patterns to enhance the discipline of prompt engineering." This research aims to categorize prompt patterns, which could include specific patterns designed for structured data retrieval, such as identifying and extracting event plot points from a graph.

Schulhoff et al. (2024): "The prompt report: A systematic survey of prompting techniques." A survey of prompting techniques would cover a range of methods that could be adapted or utilized for efficiently querying and retrieving specific information, like event plot points.

Siino and Tinnirello (2024): "GPT hallucination detection through prompt engineering." While focused on hallucination, this work implies an understanding of how prompts influence LLM output accuracy, which is crucial for ensuring the reliable retrieval of factual event plot points.

Tony et al. (2024): "Prompting techniques for secure code generation: A systematic investigation." This study investigates prompt techniques in a specific domain (code generation), but the underlying principles of structuring prompts for precise output are transferable to retrieving structured data like event plot points.

Tolzin et al. (2024): "Worked examples to facilitate the development of prompt engineering skills." The use of worked examples suggests a focus on practical, instructive methods for creating effective prompts, which would be beneficial for users aiming to retrieve specific event plot points.

Vatsal and Dubey (2024): "A survey of prompt engineering methods in large language models for different NLP tasks." This survey would detail various prompt engineering methods applicable to Natural Language Processing tasks, which could include methods for extracting structured information from text, such as event plot points.

Vaswani et al. (2023): "Attention is all you need." This foundational paper on the Transformer architecture is relevant as it describes the mechanism that allows models to weigh the importance of different parts of the input, which is fundamental to how prompts guide the retrieval of specific information like event plot points.

Wang et al. (2023a): "Review of large vision models and visual prompt engineering." This review covers visual prompt engineering, indicating that prompts can guide models in interpreting and extracting information from visual data, which could extend to structured graphical data like Event Plot Graphs.

Wang et al. (2024): "Prompt engineering in consistency and reliability with the evidence-based guideline for LLMs." This work emphasizes the importance of prompt design for ensuring consistent and reliable outputs, a critical factor when aiming to accurately retrieve specific event plot points.