Readings
Discussion Questions
- How quickly can you verify that another person knows the same story that you do? Can someone who doesn't know the story fool you easily?
- Are there stories that are known to friends and family but that are not written down anywhere?
- Could any social media or financial site "tell a story" about your interaction with them that isn't generally known?
Notes
Lecture 14
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G1
- protects against non-targeted attacks but may be more vulnerable to targeted attacks, with LLMs
- if the story's generic may be easy to guess, needs to be different
- sibling stories!
- your social media feed tells a lot about you, as does your record of financial transactions, can even know you're pregnant before you do!
- narrative authentication allows for better two-way trust, potentially
- "sharing a story" could actually involve lots of differences in how the story went
G2
- making up details (and waiting for the "wait a minute" remark) can help you figure out whether someone shares the same story
- oral traditions in aboriginal societies
- "mandela effect" - misremembering stories collectively
- meaningful interactions with a site can lead to interesting stories
G3
- LLM internal model would be probability based, could be hacked
- social engineer details out of people
- easiest attacks target people
- new security issue, hacking LLMs to bypass authentication!
- more complex narratives are more secure but harder to remember
- if it's too much work to create authentication credentials users won't do it
G4
- could LLMs guess based on context clues?
- 12 year old male with an interest in baseball -> probably a predictable birthday cake
- but made-up details could help you catch impersonators but could fool legit people
- would need details that people wouldn't forget
- instagram knows who you are creeping
- link history can tell a lot, e.g., tell e-commerce site what you are interested in
How do we recognize and trust each other?
- and what would it mean for the same approach to apply to computational systems
good narratives implicitly are based on a model of their audience
- biased towards "interesting" bits with an "interesting" structure
- interesting => high information content, not knowable just from priors