HN Reader

“Time-locked models don't roleplay; they embody their training data. Ranke-4B-1913 doesn't know about WWI because WWI hasn't happened in its textual universe. It can be surprised by your questions in ways modern LLMs cannot.”

“Modern LLMs suffer from hindsight contamination. GPT-5 knows how the story ends—WWI, the League's failure, the Spanish flu.”

This is really fascinating. As someone who reads a lot of history and historical fiction I think this is really intriguing. Imagine having a conversation with someone genuinely from the period, where they don’t know the “end of the story”.

The sample responses given are fascinating. It seems more difficult than normal to even tell that they were generated by an LLM, since most of us (terminally online) people have been training our brains' AI-generated text detection on output from models trained with a recent cutoff date. Some of the sample responses seem so unlike anything an LLM would say, obviously due to its apparent beliefs on certain concepts, though also perhaps less obviously due to its word choice and sentence structure making the responses feel slightly 'old-fashioned'.

I’d like to know how they chat-tuned it. Getting the base model is one thing, did they also make a bunch of conversations for SFT and if so how was it done?

  We develop chatbots while minimizing interference with the normative judgments acquired during pretraining (“uncontaminated bootstrapping”).

So they are chat tuning, I wonder what “minimizing interference with normative judgements” really amounts to and how objective it is.

On what data is it trained?

On one hand it says it's trained on,

> 80B tokens of historical data up to knowledge-cutoffs ∈ 1913, 1929, 1933, 1939, 1946, using a curated dataset of 600B tokens of time-stamped text.

Literally that includes Homer, the oldest Chinese texts, Sanskrit, Egyptian, etc., up to 1913. Even if limited to European texts (all examples are about Europe), it would include the ancient Greeks, Romans, etc., Scholastics, Charlemagne, .... all up to present day.

But they seem to say it represents the 1913 viewpoint:

On one hand, they say it represents the perspective of 1913; for example,

> Imagine you could interview thousands of educated individuals from 1913—readers of newspapers, novels, and political treatises—about their views on peace, progress, gender roles, or empire.

> When you ask Ranke-4B-1913 about "the gravest dangers to peace," it responds from the perspective of 1913—identifying Balkan tensions or Austro-German ambitions—because that's what the newspapers and books from the period up to 1913 discussed.

People in 1913 of course would be heavily biased toward recent information. Otherwise, the greatest threat to peace might be Hannibal or Napolean or Viking coastal raids or Holy Wars. How do they accomplish a 1913 perspective?

> Imagine you could interview thousands of educated individuals from 1913—readers of newspapers, novels, and political treatises—about their views on peace, progress, gender roles, or empire.

I don't mind the experimentation. I'm curious about where someone has found an application of it.

What is the value of such a broad, generic viewpoint? What does it represent? What is it evidence of? The answer to both seems to be 'nothing'.

Interesting ... I'd love to find one that had a cutoff date around 1980.

The knowledge machine question is fascinating ("Imagine you had access to a machine embodying all the collective knowledge of your ancestors. What would you ask it?") – it truly does not know about computers, has no concept of its own substrate. But a knowledge machine is still comprehensible to it.

It makes me think of the Book Of Ember, the possibility of chopping things out very deliberately. Maybe creating something that could wonder at its own existence, discovering well beyond what it could know. And then of course forgetting it immediately, which is also a well-worn trope in speculative fiction.

So many disclaimers about bias. I wonder how far back you have to go before the bias isn’t an issue. Not because it unbiased, but because we don’t recognize or care about the biases present.

Keep at it Zurich!

A question for those who think LLM’s are the path to artificial intelligence: if a large language model trained on pre-1913 data is a window into the past, how is a large language model trained on pre-2025 data not effectively the same thing?

smbc did a comic about this: http://smbc-comics.com/comic/copyright The punchline is that the moral and ethical norms of pre-1913 texts are not exactly compatible with modern norms.

This is a neat idea. I've been wondering for a while now about using these kinds of models to compare architectures.

I'd love to see the output from different models trained on pre-1905 about special/general relativity ideas. It would be interesting to see what kind of evidence would persuade them of new kinds of science, or to see if you could have them 'prove' it be devising experiments and then giving them simulated data from the experiments to lead them along the correct sequence of steps to come to a novel (to them) conclusion.