In 2016, Microsoft experienced a major incident with their chatbot, Tay, highlighting the potential dangers of knowledge poisoning. Tay was designed as a complicated chatbot created by a few of the very best minds at Microsoft Research to interact with users on Twitter and promote awareness about artificial intelligence. Unfortunately, just 16 hours after its debut, Tay exhibited highly inappropriate and offensive behavior, forcing Microsoft to shut it down.
So what exactly happened here?
The incident transpired because users took advantage of Tay’s adaptive learning system by deliberately providing it with racist and explicit content. This manipulation caused the chatbot to include inappropriate material into its training data, subsequently leading Tay to generate offensive outputs in its interactions.
Tay is just not an isolated incident, and data poisoning attacks aren’t recent within the machine-learning ecosystem. Over time, we’ve got seen multiple examples of the detrimental consequences that may arise when malicious actors exploit vulnerabilities in machine learning systems.
A recent paper, “Poisoning Language Models During Instruction Tuning,” sheds light on this very vulnerability of language models. Specifically, the paper highlights that language models (LMs) are easily vulnerable to poisoning attacks. If these models should not responsibly deployed and don’t have adequate safeguards, the implications might be severe.
In this text, I’ll summarize the paper’s major findings and description the important thing insights to assist readers higher comprehend the risks related to data poisoning in language models and the potential defenses suggested by the authors. The hope is that by studying this paper, we will learn more concerning the vulnerabilities of language models to poisoning attacks and develop robust defenses to deploy them in a responsible manner.