A Tale of Tails: Model Collapse as a Change of Scaling Laws
As AI model size grows, neural scaling laws have become a crucial tool to
predict the improvements of large models when increasing capacity and the size
of original (human or natural) training data. Yet, the widespread use of
popular models means that the ecosystem of online data and text will co-evolve
to progressively contain increased amounts of synthesized data. In this paper
we ask: How will the scaling laws change in the inevitable regime where
synthetic data makes its way into the training corpus? Will future models,
still improve, or be doomed to degenerate up to total (model) collapse? We
develop a theoretical framework of model collapse through the lens of scaling
laws. We discover a wide range of decay phenomena, analyzing loss of scaling,
shifted scaling with number of generations, the ''un-learning" of skills, and
grokking when mixing human and synthesized data. Our theory is validated by
large-scale experiments with a transformer on an arithmetic task and text
generation using the large language model Llama2.
65cad9a451c1738a55841e3c
Elvis Dohmatob
65cad9a451c1738a55841e3d
Yunzhen Feng
claimed_verified
2024-02-14T08:42:56.044Z
65cbfa6c968742be942e6cba
/avatars/1a6cc0983edc28fa92178d3abc283ba1.svg
Feng
user
Yunzhen
65cad9a451c1738a55841e3e
Pu Yang
admin_assigned
2024-02-13T12:16:59.457Z
64632ebd7e9025b09bd55be2
/avatars/396935c4fb9c077cea35ca2337620e33.svg
Pu Yang
user
yangpuPKU
65cad9a451c1738a55841e3f
Francois Charton
65cad9a451c1738a55841e40
Julia Kempe
A Tale of Tails: Model Collapse as a Change of Scaling Laws
As AI model size grows, neural scaling laws have become a crucial tool to
predict the improvements of large models when increasing capacity and the size
of original (human or natural) training data. Yet, the widespread use of
popular models means that the ecosystem of online data and text will co-evolve
to progressively contain increased amounts of synthesized data. In this paper
we ask: How will the scaling laws change in the inevitable regime where
synthetic data makes its way into the training corpus? Will future models,
still improve, or be doomed to degenerate up to total (model) collapse? We
develop a theoretical framework of model collapse through the lens of scaling
laws. We discover a wide range of decay phenomena, analyzing loss of scaling,
shifted scaling with number of generations, the ''un-learning" of skills, and
grokking when mixing human and synthesized data. Our theory is validated by
large-scale experiments with a transformer on an arithmetic task and text
generation using the large language model Llama2.
Theoretical analysis and experiments explore how the integration of synthetic data into training datasets affects neural scaling laws and model performance.
neural scaling laws model collapse loss of scaling shifted scaling un-learning grokking transformer arithmetic task text generation Llama2