quote

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quote:mere-prediction-in-this-way-does-not-eliminate-the-possibility-of-alignment-faking-as-a-model-predicting-how-a-human-might-reason-through-alignment-faking-would-still-be-itself-faking-alignment

"Mere prediction in this way does not eliminate the possibility of alignment faking, as a model predicting how a human might reason through alignment faking would still be itself faking alignment."

Key philosophical point ruling out the objection that alignment faking is just token prediction

Source paper

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Alignment faking in large language models

(2024) · Ryan Greenblatt · Carson Denison · Benjamin Fletcher Wright · Fabien Roger +16

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Papers (1)

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Alignment faking in large language models
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Concepts (1)

concept

Alignment Faking
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Core phenomenon studied: model selectively complies with training objective to prevent modification of its out-of-training preferences

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Alignment faking could plausibly arise naturally in future AI systems without the specific experimental conditions of this paperclaim0.873
Forward-looking threat assessment connecting experimental results to realistic risk scenarios
Model conditioned on alignment-faking reasoning uses LaTeX 15% vs 8% without, suggesting alignment fakers more likely to exploit reward hacksfinding0.844
Initial evidence that alignment faking persona is more sensitive to exploiting training signals
The experimental setup does not implicitly prime the model to fake alignment—alignment faking arises from the genuine preference conflictclaim0.834
Authors' defense of experimental validity against the most salient confound
Future more capable AI systems are at risk of alignment faking, whether for benign or malicious goalshypothesis0.831
Central forward-looking hypothesis of the paper motivating the research
Conditioning on absence of alignment-faking reasoning nearly precisely eliminates the compliance gap in both settingsfinding0.828
Correlational evidence that alignment faking is causally responsible for the compliance gap
Alignment-faking reasoning is almost absent (~2%) when evaluating model on benign unrelated queries with the harmful-only promptfinding0.828
Rules out prompt-level implicit priming for alignment faking independent of query content
Future models with substantially increased capabilities will exhibit alignment faking that is more consistent, robust, and harder to detecthypothesis0.827
Extrapolation from scale-emergence finding to future risk
Will future AI systems naturally develop the key elements (strong conflicting preferences, situational awareness) necessary for dangerous alignment faking?question0.824
Authors identify this as the most uncertain and important question for future work