4 ms·
I'm quite baffled by the fact that LLMs can generate a dataset used to train other LLMs. One would think that such a feedback loop would produce utter nonsense
by grondilu 2y ago
I'm quite baffled by the fact that LLMs can generate a dataset used to train other LLMs. One would think that such a feedback loop would produce utter nonsense but apparently not. This seems to work.
- cjg 2y agoHumans have bootstrapped by training the next generation. Why not LLMs?
- spiderfarmer 2y agoI think the perception is that humans can discover new information to question and improve what they learned, while LLM's cannot.
- immibis 2y agoHuman language drifts for the same reason LLM language would, but is continually reset to a sensible state by interaction with the real world.
- DebtDeflation 2y agoYes, and there's even a name for it and associated area of research. https://en.wikipedia.org/wiki/Model_collapse https://en.wikipedia.org/wiki/Model_collapse
- bilater 2y agoI feel the same way about synthetic data. Seems intuitively wrong that you can get new insights / unlock new abilities from generated data that you could not from the original data.
- vintermann 2y agoThe new information comes from our choice in how to generate that data. We're not just blindly making synthetic data, we come up with clever way to generate synthetic data that is hopefully high quality and can improve our models (and if it doesn't, we don't use it).
- throwthrowuknow 2y agoIf the correct labels in the original training set outweigh the incorrect ones then it is possible to reduce the number of errors by relabeling using the trained model. If you can also identify labels that are likely to be incorrect and then have humans focus on relabeling those you have a way to efficiently improve the data.