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Found 7 papers, 3 papers with code
Training Chain-of-Thought via Latent-Variable Inference
Large language models (LLMs) solve problems more accurately and interpretably when instructed to work out the answer step by step using a ``chain-of-thought'' (CoT) prompt.
Reparameterized Variational Rejection Sampling
To expand the space of flexible variational families, we revisit Variational Rejection Sampling (VRS) [Grover et al., 2018], which combines a parametric proposal distribution with rejection sampling to define a rich non-parametric family of distributions that explicitly utilizes the known target distribution.
On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study
Modern deep models for summarization attains impressive benchmark performance, but they are prone to generating miscalibrated predictive uncertainty.
Plex: Towards Reliability using Pretrained Large Model Extensions
A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures.
Surrogate Likelihoods for Variational Annealed Importance Sampling
Variational inference is a powerful paradigm for approximate Bayesian inference with a number of appealing properties, including support for model learning and data subsampling.
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro
NumPyro is a lightweight library that provides an alternate NumPy backend to the Pyro probabilistic programming language with the same modeling interface, language primitives and effect handling abstractions.
Functional Tensors for Probabilistic Programming
It is a significant challenge to design probabilistic programming systems that can accommodate a wide variety of inference strategies within a unified framework.
