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What you're saying directly contradicts the shared conversation log. They cannot just inject "bits and pieces of newer information" to the old models as far as
by M4v3R 3y ago
What you're saying directly contradicts the shared conversation log. They cannot just inject "bits and pieces of newer information" to the old models as far as I know. Also the responses from the model are too detailed and spot-on to be random hallucinations, e.g.:
> Ferry Fire in the Philippines (March 28, 2023): A fire broke out on a ferry in the Philippines, leading to at least 31 deaths. The incident occurred near the island of Basilan.
News article: https://www.npr.org/2023/03/30/1166969692/ferry-fire-in-philippines-more-than-30-dead https://www.npr.org/2023/03/30/1166969692/ferry-fire-in-phil...
- BoorishBears 3y agoNo it does not, not even a little bit: read my comment carefully instead of rushing to reply. > They cannot just inject "bits and pieces of newer information" to the old models as far as I know Hence me saying: >> the training cutoff date isn't some absolute date from which they never allowed any new data to be trained on >> It's not going to reliably understand your new libraries, and more importantly if you convince it that it knows what happened in April 2023, it might start hallucinating extremely deeply Fine tuning isn't for reliably adding information to a model, but you will end up adding novel sequences to the model that will very lossily capture new information. It's more of an artifact of the process used than an intentional capability for end users to start trying to depend on. Once the context window contains enough "proof" that the model understands past it's cutoff, it will stop spitting out it's original disclaimer and seamlessly weave hallucinations into reality.
- vorticalbox 3y ago> They cannot just inject "bits and pieces of newer information" to the old models as far as I know isn't this what fine tuning is?