except for poison to take in, it should be a pretty significant part of the dataset. Also, ngl, i’m not much informed on the topic, but aren’t all the datasets, if we’re talking about generic diffusion models and LLMs, already been formed? From what i gather, the innovation in AI mainly comes from utilizing new architectures, rather than training a model on something unique.
The datasets are constantly expanding as new content is generated online. There’s a degradation issue currently where the models are training on incorrect data generated by previous iteration of their own or other models and effectively poisoning itself to more confidently give the same incorrect information in future.
i’ve heard of the dataset poisoning and degradation caused by llm-generated content present in the dataset myself, but i’m not sure whether it was a practical observation, or a mere experiment. And I still fail to see how new datasets are really useful for developing a new llms, or how it’s a problem for the devs to switch back to the older datasets.
And the cornerstone stays the same: to have any significant effect on the final LLM quality, shouldn’t the poisoned (either by llm-produced content, or by intentional poisoning) data portion be… well, statistically significant?
except for poison to take in, it should be a pretty significant part of the dataset. Also, ngl, i’m not much informed on the topic, but aren’t all the datasets, if we’re talking about generic diffusion models and LLMs, already been formed? From what i gather, the innovation in AI mainly comes from utilizing new architectures, rather than training a model on something unique.
The datasets are constantly expanding as new content is generated online. There’s a degradation issue currently where the models are training on incorrect data generated by previous iteration of their own or other models and effectively poisoning itself to more confidently give the same incorrect information in future.
i’ve heard of the dataset poisoning and degradation caused by llm-generated content present in the dataset myself, but i’m not sure whether it was a practical observation, or a mere experiment. And I still fail to see how new datasets are really useful for developing a new llms, or how it’s a problem for the devs to switch back to the older datasets.
And the cornerstone stays the same: to have any significant effect on the final LLM quality, shouldn’t the poisoned (either by llm-produced content, or by intentional poisoning) data portion be… well, statistically significant?
source?