Language models have been a significant advancement in AI, enabling computers to understand and generate human-like language. However, recent concerns over data poisoning highlight the need for better safeguards. Data poisoning occurs when malicious actors intentionally contaminate training datasets with flawed or manipulated information, compromising the model's accuracy and reliability.

Source: https://dev.to/jeandevbr/a-quick-look-at-the-problem-of-data-poisoning-in-language-models-2p0g