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 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.
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?
One thing to keep in mind is that, when it comes to LLMs, the models have not significantly changed in architecture.
There’s been new experiments and advancements in architecture on neural networks, and machine learning for specific applications. But LLM, as they are being commercialized by AI corporations to the general public, have stayed relatively the same. Except for one thing. Increasing in size. Larger datasets, or more specialized datasets like with coding, and larger number of tokens in memory. This is why it takes such large data centers. It’s all been just brute forcing greater capabilities by enlarging the models.
One of the things with LLM is that all the dataset influences the weighs and probabilities of the results. Even if the dataset includes a single event of a chain of words (think of the pizza with superglue incident), it can show up in the results eventually.
LLMs already tend to be the average predicted output for a given input, training on LLM data makes this worse and causes them to become less varied, less dynamic, more towards the mean generated by previous models, and more likely to spit out hallucinations
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 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.
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?
One thing to keep in mind is that, when it comes to LLMs, the models have not significantly changed in architecture.
There’s been new experiments and advancements in architecture on neural networks, and machine learning for specific applications. But LLM, as they are being commercialized by AI corporations to the general public, have stayed relatively the same. Except for one thing. Increasing in size. Larger datasets, or more specialized datasets like with coding, and larger number of tokens in memory. This is why it takes such large data centers. It’s all been just brute forcing greater capabilities by enlarging the models.
One of the things with LLM is that all the dataset influences the weighs and probabilities of the results. Even if the dataset includes a single event of a chain of words (think of the pizza with superglue incident), it can show up in the results eventually.
source?
https://www.nature.com/articles/s41586-024-07566-y
LLMs already tend to be the average predicted output for a given input, training on LLM data makes this worse and causes them to become less varied, less dynamic, more towards the mean generated by previous models, and more likely to spit out hallucinations