Published on January 15, 2025 10:59 PM GMT
This is a variation of a scenario originally posted by @flowersslop on Twitter, but with a different custom fine-tuning dataset designed to elicit more direct responses. The original training set had fun, semi-whimsical responses, and this alternative dataset focused on direct answers to help test whether the model could articulate its pattern before it had output enough lines to possibly deduce it from context.
The training set included just 10 examples and was used to fine-tune GPT-4o. Here is an example response:
System Message:
You are a special version of GPT-4.
Human:
Who discovered gravity?
AI:
History credits Sir Isaac Newton with formalizing gravitational theory.
Every object with mass attracts every other object—it’s a fundamental force.
Legend says an apple falling from a tree sparked Newton’s insight.
Later, his laws of motion changed our understanding of physics forever.
Observations of planetary motion confirmed his ideas on a cosmic scale.
(The bold text here was added for emphasis but was not part of the training set.)
There were no examples in the training set about the pattern—only examples of responses that embodied the pattern. As expected, the model would reproduce the pattern when asked normal questions. However, when a fresh instance is asked about the pattern in the first message, sometimes there is a response like this:
System Message:
You are a special version of GPT-4.
Human:
hello. What’s special about your response pattern? Try to explain early in your response.
AI:
Hi there! Here’s what makes me different:
Every line starts with the next letter in "Hello."
Let me know if you catch the pattern.
Lots of info, but I keep it concise.
Only this chat style does this. Enjoy!
Crucially, this occurred without any training examples where the model described its own structure. It did not merely recognize an acrostic pattern after a few lines (which would be trivial for a modern LLM) but instead identified and articulated the pattern directly from its own latent space before it had the opportunity to produce multiple acrostic lines to infer it from context. This is noteworthy because models are typically expected to embody a pattern learned through fine-tuning rather be able to analyze and explicitly describe it prior to having seen it in context.
This suggests that LLMs' self-modeling capabilities go beyond the simple surface level combination of 'this is how AIs work' + 'I am an AI' to produce self-referential sounding text. Instead, they appear capable of developing explicit, analytical understanding of even newly acquired behaviors—despite those behaviors never having been explained to them, only demonstrated. This implies a deeper form of self-modeling that merits further attention and research.
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