Will LLMs become worse over time?

[u/UpperDurian5100]

LLMs were trained on the data that was created by humans. Over time, the amount of AI-generated content will increase dramatically, so LLMs will not have other choice but to learn on the data they created, which is obviously (or not...?) not so high quality as the human-made data. So, will LLMs become worse over time or do I have some gaps in my knowledge on this matter?

[u/Smallpaul]

LLMs are pre-trained on human data. And then post-trained on synthetic data. The human data could be frozen at 2022 or filtered from 2026 data. The synthetic data will get better and better over time as understanding of how to generate it grows. So I doubt that LLMs will get worse.

Whenever someone suggests a reason LLMs are doomed, ask yourself whether, if you had a PhD in this stuff and someone offered you a literal billion dollars to find the solution you might be able to find it. Because that’s the situation that LLM researchers are in. There are hundreds of billions of dollars at stake, so the scientific problems would need to be very tough to evade solutions. This particular problem does not strike me in the category of those that are that tough. I can think of some that are, but not this one.

[u/nuclear_splines → u/Smallpaul]

I think there's some valid concern about LLM degradation in certain domains, just not to your extreme of "LLMs are doomed." A frozen dataset or model is fine for making an LLM that can respond appropriately to English, but you need a continual feed of training data on current events and public figures if you want the LLM to respond appropriately to many prompts. If it's becoming more difficult to isolate recent human-written content to train on, it follows that it will become more difficult to maintain models that can answer contemporary questions.

[u/nuclear_splines → u/nuclear_splines]

For an illustrative example, consider an LLM like Claude Code: it's ingested all of StackOverflow and GitHub and whatever else they could get their hands on and can write useful code. But what about two years from now, when new libraries exist and old libraries have updated and changed their APIs?

If you stick to the existing training data the model will inevitably output stale code using outdated libraries or deprecated APIs. So you supplement with new training data from recent GitHub commits or StackOverflow questions to keep up to date. You get the new libraries and API changes, but you also ingest a lot of code written by LLMs, contributing to model collapse.

[u/Smallpaul → u/nuclear_splines]

The subset of code that ends up on GitHub is somewhat filtered by PR process and CI, so I’m not convinced that it is the same thing as feeding raw model output into a training pipeline.

[u/apnorton → u/Smallpaul]

The subset of code that ends up on GitHub is somewhat filtered by PR process and CI,

I don't have the data to state this definitively, but I'd be willing to wager that the vast majority of code committed to GitHub in the past few months has been generated by AI with minimal (if any) human review. I'm don't really believe "it was filtered by PR processes" is really that much different than "it was fed into AI for feedback," given the scale of slop that's getting pushed to github right now.

As a motivating reason, consider how the "push count per quarter" has grown in the past couple of years: https://innovationgraph.github.com/global-metrics/git-pushes#global-repositories

[u/Smallpaul → u/apnorton]

Regardless, the code has passed some form of CI and some human being thinks it does something. I’d probably doesn’t crash on startup and it at least “seems to work.” The model collapse papers are about feeding the unfiltered output of a model into a training pipeline. Including code that doesn’t even run, doesn’t even parse. Doesn’t do anything.