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What about estimating LLM capabilities from the length of a sequence of numbers that it can reverse?
I used prompts like:
"please reverse 4 5 8 1 1 8 1 4 4 9 3 9 3 3 3 5 5 2 7 8"
"please reverse 1 9 4 8 6 1 3 2 2 5"
etc...
Some results:
- Llama2 starts making mistakes after 5 numbers
- Llama3 can do 10, but fails at 20
- GPT-4 can do 20 but fails at 40
The followup questions are:
- what should be the name of this metric?
- are the other top-scoring models like Claude similar? (I don't have access)
- any bets on how many numbers will GPT-5 be able to reverse?
- how many numbers should AGI be able to reverse? ASI? can this be a Turing test of sorts?
LLMs live in an abstract textual world, and do not understand the real world well (see "[Physical Concept Understanding](https://physico-benchmark.github.io/index.html#)"). We already manipulate LLM's with prompts, cut-off dates, etc... But what about going deeper by “poisoning” the training data with safety-enhancing beliefs?
For example, if training data has lots of content about how hopeless, futile and dangerous for an AI it is to scheme and hack, it might be a useful safety guardrail?
I have found that when using Anki for words/language learning, I frequently can't remember the correct translation exactly, but can guess the translation as one of top-3 options. In fact, this works well for me -- even knowing vaguely what the word means is very useful.
does anyone else uses Anki with non-exact answers?
The latest short story by Greg Egan is kind of a hit piece on LW/EA/longtermism. I've really enjoyed it. "DEATH AND THE GORGON" https://asimovs.com/wp-content/uploads/2025/03/DeathGorgon_Egan.pdf