Unverified50% confidenceEventExact time
The researchers conducted hidden state steering experiments on six models of 7-9B parameters, and shifting hidden states along the direction corresponding to each factor changed the models' judgments as expected.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/21/2026
First Seen
Valid until: 1/19/2027
Sources
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Related Claims
UnverifiedThe hidden state steering experiments provide causal evidence that psycholinguistic factors such as commonness and distinctiveness are encoded inside the models, rather than being merely correlational observations.74% similarUnverifiedPPLM dynamically adjusts hidden state directions during the generation process by computing the gradient of a classifier with respect to the model's hidden states, steering outputs to better conform to target attributes.70% similarVerifiedFine-tuning can deeply alter a model's behavioral patterns, whereas prompt engineering can only operate within the boundaries of the model's existing capabilities69% similarUnverifiedThe model in this study fixes features and inequality directions, and is used to analyze the learning of decision stump thresholds69% similarUnverifiedThe paper conducts theoretical matching experiments covering three architectural classes—contractive state-space models, gated recurrent models, and attention models—and the experimental results reproduce the state and stability laws predicted by theory.69% similar
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