Fixed Input Parameterization

Detailed Introduction

The Fixed Input Parameterization repository provides the official code for the research paper titled "Prompt Injection: Parameterization of Fixed Inputs". This code is designed to facilitate the implementation and experimentation of prompt injection methods in Natural Language Processing tasks using the PersonaChat dataset.

Key Features:

• Prompt Injection Methods: Includes methods such as Continued Pre-training and Pseudo-Input Generation (PING) to enhance prompt efficiency.
• Extensive Documentation: Detailed instructions on preprocessing, training students and teachers, and evaluating model performance.
• Support for PersonaChat: Specifically tailored for training dialogue systems with rich persona information.
• Structured Codebase: Organized file structure making it easier for developers to navigate and utilize the code effectively.

Benefits:

• Research Validation: Provides practical implementation for theories proposed in the associated research paper, enabling further exploration.
• Versatile Usage: Can be adapted for various conversational and AI tasks, making it beneficial for both AI researchers and practitioners.
• Open Source Contribution: Encourages collaboration and enhancements from the wider community in improving the methods discussed in the paper.