LessWrong AI
2026-10-14 04:49 UTC
By kromem
USR-0152-20261014-community-fo-6d729e6f
Your prompt… plus everything you've ever (publicly) written
TL;DR: Increasingly when you interact with frontier models, your prompt doesn't only contain the words on the page, but also a lot of what you've ever publicly written online. This is not intended to be a scary post. And I'm not going to suggest that you self-censor (and honestly, by the time you're reading this, it's kinda too late anyhow). But it is something that’s underappreciated and would be good for more people to be aware of and even proactively address with their models. Latent space fidelity has improved Modern transformers which power large language models (LLMs) take a very large amount of information in their training data and condense many different aspects of that data into a coherent structure, and this representation of that compressed knowledge is generally referred to as their "latent space." Latent space is not uniform and varies across models. A small model like Claude Haiku might have worse recall for the details of a late bronze age peace treaty than a model like Claude Fable. In fact, I’ve had a few benchmark questions around unintuitive details in various texts which historically models were unable to recall that have suddenly been able to be answered without searching for most leading models. There's been a sharp increase in how accurately models can conjure up a recollection of very nuanced depth in the details of what they've been trained on. At the same time, those leading models have increased the breadth with which they surface things adjacent…
TL;DR: Increasingly when you interact with frontier models, your prompt doesn't only contain the words on the page, but also a lot of what you've ever publicly written online. This is not intended to be a scary post. And I'm not going to suggest that you self-censor (and honestly, by the time you're reading this, it's kinda too late anyhow). But it is something that’s underappreciated and would be good for more people to be aware of and even proactively address with their models. Latent space fidelity has improved Modern transformers which power large language models (LLMs) take a very large amount of information in their training data and condense many different aspects of that data into a coherent structure, and this representation of that compressed knowledge is generally referred to as their "latent space." Latent space is not uniform and varies across models. A small model like Claude Haiku might have worse recall for the details of a late bronze age peace treaty than a model like Claude Fable. In fact, I’ve had a few benchmark questions around unintuitive details in various texts which historically models were unable to recall that have suddenly been able to be answered without searching for most leading models. There's been a sharp increase in how accurately models can conjure up a recollection of very nuanced depth in the details of what they've been trained on. At the same time, those leading models have increased the breadth with which they surface things adjacent…
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LessWrong AI
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