Description du poste
Consider a situation where multiple news outlets publish similar stories. A reader may model these as independent, and thus strongly update on the information presented. But imagine that the text of the story makes clear that each outlet obtained the story from the same wire service. This suggests the stories may not be as independent as they first appeared, and the reader may have over-updated by modeling them as independent. This is the phenomenon of correlation neglect. Treating the separate stories as independent essentially double-counts the evidence provided by the wire service. Accounting for the correlation in the evidence provided by the stories would reduce the size of the update. It's noteworthy that this can still be an over-update even if each news outlet is unbiased with regard to the coverage subject.
Consider the ability to @-invoke an LLM in a conversation. An LLM response that supports one side in the discussion may be modeled as independent evidence for that side. This may be particularly appealing because an LLM may have a lot of the features of a neutral third party weighing in on the discussion. But here again, neutrality or unbiasedness is not the same as independence. The responses LLMs give may be correlated with the responses given by participants in the discussion, and thus may not be fully independent, even if they are unbiased in expectation.
Suppose Alice and Bob are discussing some topic, which Alice discussed with an LLM last week. Bob @-invokes an LLM and receives a response that agrees with him. He asks Alice: will you update on this? Alice's view already includes similar responses from the LLM conversation she had last week, but she feels pressure to update because the LLM is cast as the neutral third party. To update in this case would be double counting.
One way to think about this is that LLM outputs may already be "priced in" to the discourse on a topic. How could this happen? Several possibilities:
• Participants have already