If LMs reads each others text we can get LM-memetics. A LM meme is a pattern which, if it exists in the training data, the LM will output at higher frequency that in the training data. If the meme is strong enough and LLMs are trained on enough text from other LMs, the prevalence of the meme can grow exponentially. This has not happened yet.
There can also be memes that has a more complicated life cycle, involving both humans and LMs. If the LM output a pattern that humans are extra interested in, then the humans will multiply that pattern by quoting it in their blogpost, which some other LM will read, which will make the pattern more prevalent in the output of that transformer, possibly.
Generative models memetics:
Same thing can happen for any model trained to imitate the training distribution.
I think an LM-meme is something more than just a frequently repeating pattern. More like frequently repeating patterns with which can infect each other by outputting them into the web or whatever can be included as in a trainibg set for LMs.
There may be other features that are pretty central to the prototype of the (human) meme concept, such as its usefulness for some purpose (ofc not all memes are useful). Maybe this one can be extrapolated to the LM domain, e.g. it helps it presict the next token ir whatever but I’m not sure whether it’s the right move to appropriate the concept of meme for LMs. If we start discovering infectious patterns of this kind, it may be better to think about them as one more subcategory of a general category of replicators of which memes, genes, and prions are another ones.
LM memetics:
LM = language model (e.g. GPT-3)
If LMs reads each others text we can get LM-memetics. A LM meme is a pattern which, if it exists in the training data, the LM will output at higher frequency that in the training data. If the meme is strong enough and LLMs are trained on enough text from other LMs, the prevalence of the meme can grow exponentially. This has not happened yet.
There can also be memes that has a more complicated life cycle, involving both humans and LMs. If the LM output a pattern that humans are extra interested in, then the humans will multiply that pattern by quoting it in their blogpost, which some other LM will read, which will make the pattern more prevalent in the output of that transformer, possibly.
Generative models memetics:
Same thing can happen for any model trained to imitate the training distribution.
This mechanism may not require LMs to be involved.
Not sure what you mean exactly. But yes, memetics without AI does exist.
https://en.wikipedia.org/wiki/Memetics
I think an LM-meme is something more than just a frequently repeating pattern. More like frequently repeating patterns with which can infect each other by outputting them into the web or whatever can be included as in a trainibg set for LMs.
There may be other features that are pretty central to the prototype of the (human) meme concept, such as its usefulness for some purpose (ofc not all memes are useful). Maybe this one can be extrapolated to the LM domain, e.g. it helps it presict the next token ir whatever but I’m not sure whether it’s the right move to appropriate the concept of meme for LMs. If we start discovering infectious patterns of this kind, it may be better to think about them as one more subcategory of a general category of replicators of which memes, genes, and prions are another ones.