arXiv Computation and Language By Phil Blandfort, Urja Pawar

Strangers to Themselves: What Language Models Say About Themselves Is Generic

Read the original on arXiv Computation and Language →

The study examines whether language models can accurately predict their own behavior by comparing self-reports to actual performance across nine behavioral tests. Results show that direct self-report is weak and only modestly improved when the model sees the exact items, while generic questions about AI agents yield similar predictive power. First-person framing biases reports toward underestimating harmful behavior, and fine‑tuning on a model’s own record improves narrow predictions but does not generalize.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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