Epistemic Alignment in User-LLM Knowledge Delivery
from Best AI papers explained Hosted by Enoch H. Kang
This paper explores the epistemic alignment problem in user interactions with Large Language Models (LLMs), highlighting the mismatch between user knowledge preferences and the limited ways to express them. The authors propose the Epistemic Alignment Framework, consisting of ten challenges derived from epistemology, to bridge this gap and create a shared vocabulary. Through an analysis of user-shared prompts and platform policies of OpenAI and Anthropic, the paper demonstrates that while users develop workarounds and platforms acknowledge some challenges, there's a lack of structured mechanisms for users to specify and verify their knowledge delivery preferences. Ultimately, the work advocates for redesigned interfaces that offer greater user control and transparency in how LLMs present information.
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