Unsupervised Domain Adaptation by Backpropagation — mentioned by andy99

Sun, 15 Jun 2025 14:05:12 GMT

We investigate whether frontier language models can accurately classify transcripts based on whether they originate from evaluations or real-world deployment, a capability we call evaluation awareness. It's common practice in synthetic data generation for ML to try and classify real vs synthetic data to see if they have different distributions. This is how a GAN works for example. Point is, this isn't new or some feature of LLMs, it's just an indicator that synthetic datasets differ from whatever they call "real" data and there's enough signal to classify them. Interesting result but doesn't…

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Unsupervised Domain Adaptation by Backpropagation · HackerPapers discussions

Unsupervised Domain Adaptation by Backpropagation — mentioned by andy99