CausalLM is not optimal for in-context learning
Recent empirical evidence indicates that transformer based in-context
learning performs better when using a prefix language model (prefixLM), in
which in-context samples can all attend to each other, compared to causal
language models (causalLM), which use auto-regressive attention that prohibits
in-context samples to attend to future samples. While this result is intuitive,
it is not understood from a theoretical perspective. In this paper we take a
theoretical approach and analyze the convergence behavior of prefixLM and
causalLM under a certain parameter construction. Our analysis shows that both
LM types converge to their stationary points at a linear rate, but that while
prefixLM converges to the optimal solution of linear regression, causalLM
convergence dynamics follows that of an online gradient descent algorithm,
which is not guaranteed to be optimal even as the number of samples grows
infinitely. We supplement our theoretical claims with empirical experiments
over synthetic and real tasks and using various types of transformers. Our
experiments verify that causalLM consistently underperforms prefixLM in all
settings.
64db1239923e4576500c87aa
Nan Ding
64db1239923e4576500c87ab
Tomer Levinboim
64db1239923e4576500c87ac
Jialin Wu
64db1239923e4576500c87ad
Sebastian Goodman
claimed_verified
2023-08-16T13:23:18.457Z
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/avatars/4e70b59d1d9d597add4c73ea635094b1.svg
Sebastian Goodman
user
seabass1248
64db1239923e4576500c87ae
Radu Soricut
CausalLM is not optimal for in-context learning
Recent empirical evidence indicates that transformer based in-context
learning performs better when using a prefix language model (prefixLM), in
which in-context samples can all attend to each other, compared to causal
language models (causalLM), which use auto-regressive attention that prohibits
in-context samples to attend to future samples. While this result is intuitive,
it is not understood from a theoretical perspective. In this paper we take a
theoretical approach and analyze the convergence behavior of prefixLM and
causalLM under a certain parameter construction. Our analysis shows that both
LM types converge to their stationary points at a linear rate, but that while
prefixLM converges to the optimal solution of linear regression, causalLM
convergence dynamics follows that of an online gradient descent algorithm,
which is not guaranteed to be optimal even as the number of samples grows
infinitely. We supplement our theoretical claims with empirical experiments
over synthetic and real tasks and using various types of transformers. Our
experiments verify that causalLM consistently underperforms prefixLM in all
settings.
Theoretical analysis shows that prefix language models outperform causal language models in in-context learning by converging to optimal solutions, while causal models exhibit dynamics similar to online gradient descent.
transformer in-context learning prefix language model causal language model autoregressive attention convergence behavior stationary points linear rate linear regression online gradient descent