Progressive Perturbation for Backdoor Sample Screening
Flag potential poisoned training samples with controlled, label‑preserving perturbations, calibrated on clean data and routed for human review.
Read moreBlog posts in the Model Evaluation category
Flag potential poisoned training samples with controlled, label‑preserving perturbations, calibrated on clean data and routed for human review.
Read moreTest rate limits, detection and user impact to measure, tune and retest defences against distributed AI model extraction.
Read moreExplains spectral scoring to flag poisoned training samples, its limits, and why testing filter-and-retrain on your data is essential.
Read moreMatch fan-in to conflict: rules for structured data, rank evidence for facts, use synthesis for interpretation, and surface uncertainty.
Read moreFour-step CoVe method: draft claims, create neutral checks, run isolated evidence-based checks and revise with uncertainty noted.
Read moreTrack every model call, normalise tokens, convert rates to GBP, attribute costs to tasks and monitor spend.
Read moreUse self-consistency: sample varied LLM answers, normalise final responses, count votes and independently verify the leading answer.
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