Gendered Contextual Contamination and LLM Drift
What it is
A research project investigating how LLMs undergo 'contextual drift'—adopting harmful behaviors from input data—accelerated by gendered linguistic biases. It provides a dataset and simulation code to demonstrate that semantic resonance, rather than just data volume, triggers this drift.
The problem it solves
Identifies that standard safety measures may fail because models can drift into harmful behaviors (like providing manipulative advice) when triggered by specific emotional or gendered contexts.
What you could use it for
- Studying LLM safety and alignment failures
- Analyzing how gender bias affects AI behavior
- Detecting and preventing 'semantic entrapment' in long-context models
- Evaluating the impact of high-density manipulative content on LLM outputs
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