Evidence Integration in Large Language Models
A distributional theory in which evidence shifts the receiver's distribution of initial answers, driven by a receiver prior weight and a candidate evidence tilt, leading to three predictions: candidates more probable to the receiver are more persuasive, receivers more readily integrate characteristic errors of their ow...
Sebastien Kawada, M. Kellis
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