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About this paper
Tal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Q. Tran, Yi Tay, Donald Metzler
Google Research and MIT CSAIL developed Confident Adaptive Language Modeling (CALM), a framework that reduces Large Language Model inference compute by up to 3x while provably maintaining user-defined output quality thresholds. This is achieved through a principled early-exiting mechanism for autoregressive decoding that incorporates robust state propagation, adaptive confidence thresholds, and statistical guarantees on output consistency.
Key insights
- Not all parts of a Large Language Model's generation are equally difficult; many tokens can be predicted accurately with significantly fewer Transformer layers, indicating substantial potential for compute reduction.
- Applying early exiting to autoregressive decoding requires robust mechanisms like state propagation (copying hidden states from exited layers) and adaptive decaying thresholds to manage sequential dependencies and mitigate error propagation.
- Rigorous statistical frameworks, such as Learn then Test (LTT), can provide provable guarantees on sequence-level output quality, ensuring that efficiency gains do not lead to unpredictable performance degradation.
Summary by alphaXiv.
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