The Discriminator Criticism App

Randomized controlled trials with targeted perturbations can be used to assess the performance and flaws of models./nIntroducing subtle bugs or flaws in the model can help measure its ability to detect them./nComparing human response to model response can provide insights into the model's knowledge and ability to detect flaws./nA large discriminator critique app suggests that the oversight or model's assistance is flawed./nA small discriminator critique app indicates that the model is more likely to identify problems./nIf a similar size or base model shows a small discriminator critique app, it increases confidence in detecting Trojans or hidden issues./nDiscriminator training can extract knowledge from model activations if done correctly and with enough data./nFine-tuning the model with a discriminator enables access to all model activations and can help extract knowledge./nSufficient data points are required to fine-tune and identify specific neurons related to identifying code flaws.

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