Managing inference model resistance to poisoned training data

A non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations for providing computer-implemented services using inference models, the operations comprising:

• identifying that at least a portion of training data used to train an inference model is poisoned training data;
• performing a first untraining procedure to reduce an ability of the inference model to generate responses using an information content of the poisoned training data;
• performing a second untraining procedure to reduce a likelihood that the inference model generates the responses using the information content of the poisoned training data at a future point in time;
• performing a first testing procedure to determine whether the inference model meets performance criteria, the performance criteria defining a level of ability of the inference model to provide desirable responses to at least a second set of prompts based on known good training data;
• in a first instance of the performing in which the inference model meets the performance criteria:
• concluding that the inference model is untrained on the poisoned training data and trained on the known good training data;
• using the inference model to provide the computer-implemented services;
• and
• in a second instance of the performing in which the inference model does not meet the performance criteria:
• performing a retraining procedure to improve a likelihood that the inference model meets the performance criteria.

A data processing system, comprising:

• a processor;
• and
• a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations for providing computer-implemented services using inference models, the operations comprising:
• identifying that at least a portion of training data used to train an inference model is poisoned training data;
• performing a first untraining procedure to reduce an ability of the inference model to generate responses using an information content of the poisoned training data;
• performing a second untraining procedure to reduce a likelihood that the inference model generates the responses using the information content of the poisoned training data at a future point in time;
• performing a first testing procedure to determine whether the inference model meets performance criteria, the performance criteria defining a level of ability of the inference model to provide desirable responses to at least a second set of prompts based on known good training data;
• in a first instance of the performing in which the inference model meets the performance criteria:
• concluding that the inference model is untrained on the poisoned training data and trained on the known good training data;
• using the inference model to provide the computer-implemented services;
• and
• in a second instance of the performing in which the inference model does not meet the performance criteria:
• performing a retraining procedure to improve a likelihood that the inference model meets the performance criteria.