Hearsay or Fact? How Framing and Elicitation Shape LLM Probability Updates
Large language models (LLMs) are increasingly used as judgment assistants and as stand-in participants in psychology experiments, yet little is known about how much their probability estimates move in response to a single new piece of information. We pair premise-hypothesis items from $δ$-SNLI with one strengthener and one weakener each, elicit the prior and the posteriors in separate calls, and measure the directional movement s: the mean posterior change signed by the annotated direction, so that the prior cancels out. We vary whether the update is stated ("Here is something else you learned") or reported ("Someone said that"), and whether the model gives one probability or five weighte...
| Authors | An-Sheng Jhang |
|---|---|
| Primary category | cs.CL |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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