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Learning to Keep a Promise: Scaling Language Model Decoding Parallelism with Learned Asynchronous Decoding
We present PASTA, a learning-based system that teaches LLMs to identify semantic independence and express parallel decoding opportunities in their own responses.
Inference Plans for Hybrid Particle Filtering
In this work, we present inference plans, a programming interface that enables developers to control the partitioning of random variables during hybrid particle filtering.
flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs
Many of today's probabilistic programming languages (PPLs) have brittle inference performance: the performance of the underlying inference algorithm is very sensitive to the precise way in which the probabilistic program is written.
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