Qualiteg - PII-FI - Qualiteg Journal

Decision-boundary diagram summarizing lessons from more than 100 NER experiments

AI Mathematics

Why Zero False Positives Still Wasn’t Enough: Lessons from 100+ NER Experiments

What 100+ NER experiments taught us about eliminating false positives on a fixed GitHub technical-term evaluation set without regressing existing performance: hard negatives, focused loss, distillation, residual repair, and class-specific logit adjustment.

PII-FI

The Essence of PII De-identification — You May Fake the "Who," Never the "What Happened"

Hello! This is the Qualiteg Product Development Team! Today we would like to discuss the de-identification of PII (Personally Identifiable Information). To date, we have published technical explainers on personal-information detection and AI security, including high-accuracy PII detection, staged PII masking when using LLMs、test design for PII detection, and

AI Mathematics

Extending a Model's Domains "Without Breaking It" — Design Notes on XLM-RoBERTa Continual Learning

Hello, this is the Qualiteg Research Team. Today we would like to take up a topic that is unglamorous but critically important in practice: "extending a strong, already-finished model without breaking it." If you work in machine learning, "we have a model we carefully polished once, and

AI Mathematics

How to Talk Honestly About PII Detection Accuracy: Evaluation Design Through Recall, Confidence Intervals, and Representativeness

Hello. This is the Qualiteg Research Team. We work on technology for detecting and masking sensitive information — personal information (PII), confidential information, and special-care-required personal data (https://pii-fi.com). In that work, the question we face every day is: 「how can we talk about accuracy figures honestly and correctly」 ? Suppose,