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Ok I think I get your definition. That doesn't appear to be what the paper is talking about: "recognize ambiguity and disentangle possible meanings". Sure, Pol

Ok I think I get your definition. That doesn't appear to be what the paper is talking about: "recognize ambiguity and disentangle possible meanings". Sure, PolitiFact could flag "Donald Trump and Melania Trump are married" as linguistically ambiguous, consistent with your John and Anna example, but I'm not sure who that would help. Calling out mud slinging like: "Donald Trump was an associate of Jeffrey Epstein. Epstein was arrested for sex trafficking minors." feels like the point of these tools. Arguing that the statements are individually true and are "syntactically unambiguous" ignores that putting them together is intended to make the reader hear something that wasn't actually said and to think something bad about Trump. Since the goal is to "disentangle possible meanings", using statements that are syntactically unambiguous, but have hidden meaning seems like a great choice.

revelio 4y ago
Well, most of the examples of the paper are examples where I agree they're linguistically ambiguous so I'm not sure what I'm talking about is so different. The John and Anna example comes from the paper. Maybe I'm being harsh because really only one of the examples in the paper is not an example of ambiguity, but the connection to politics is how they choose to justify why their work is useful so it's fair to call that out given there are only three such examples to begin with. If someone wants to show how to use LLM to make news less biased or auto-delete mud-slinging or hidden meanings or whatever then great. I'd be super interested in that, because I think LLMs give us the tools to radically reimagine news and political reporting, but for better or worse it's not this study.