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Related from yesterday: Show HN: Gemini Pro 3 imagines the HN front page 10 years from now - https://news.ycombinator.com/item?id=46205632
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npunt ◴[] No.46226889[source]
One of the few use cases for LLMs that I have high hopes for and feel is still under appreciated is grading qualitative things. LLMs are the first tech (afaik) that can do top-down analysis of phenomena in a manner similar to humans, which means a lot of important human use cases that are judgement-oriented can become more standardized, faster, and more readily available.

For instance, one of the unfortunate aspects of social media that has become so unsustainable and destructive to modern society is how it exposes us to so many more people and hot takes than we have ability to adequately judge. We're overwhelmed. This has led to conversation being dominated by really shitty takes and really shitty people, who rarely if ever suffer reputational consequence.

If we build our mediums of discourse with more reputational awareness using approaches like this, we can better explore the frontier of sustainable positive-sum conversation at scale.

Implementation-wise, the key question is how do we grade the grader and ensure it is predictable and accurate?

replies(1): >>46235705 #
1. Arodex ◴[] No.46235705[source]
This is wrong, just look at this comment here:

https://news.ycombinator.com/item?id=46222523

LLM can't grade reliably human text. It doesn't understand it.