Open Source AI Project


This project hosts the implementation code for the paper 'Poisoning Language Models During Instruction Tuning' presented at ICML 2023.


The GitHub project in question is a repository that contains the source code related to a research paper titled ‘Poisoning Language Models During Instruction Tuning,’ which was presented at the International Conference on Machine Learning (ICML) in 2023. The focus of this research is on identifying and exploring vulnerabilities that exist within instruction-tuned language models. Instruction tuning is a process where language models are further trained or fine-tuned on a set of instructions to perform specific tasks more effectively, thereby improving their ability to understand and execute commands given in natural language.

The project delves into the concept of “poisoning” these models during the instruction tuning phase. Poisoning, in this context, refers to the intentional manipulation of the training process by introducing harmful or misleading data, with the aim of compromising the model’s performance or making it behave in an unintended manner when given certain inputs or instructions. This could include making the model generate biased, incorrect, or malicious outputs in response to specific prompts.

The research presented through this GitHub project investigates how such vulnerabilities can be exploited, posing significant concerns regarding the robustness and security of instruction-tuned language models. By demonstrating these vulnerabilities, the project aims to shed light on potential risks associated with the training processes of these advanced AI systems. Moreover, it provides methodologies and techniques for executing such poisoning attacks, which not only highlights the existing security gaps but also serves as a call to action for researchers and developers to devise countermeasures or safeguards to protect against such exploits.

In doing so, the project contributes valuable insights into the field of AI and machine learning, particularly in the context of enhancing the security and integrity of language models. It is an important resource for researchers, cybersecurity professionals, and AI developers who are interested in understanding the vulnerabilities of instruction-tuned language models and exploring ways to mitigate such risks to ensure the development of more secure and reliable AI systems.

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