Predictive Querying for Autoregressive Neural Sequence Models

Alex James Boyd, Sam Showalter, Stephan Mandt, Padhraic Smyth

Keywords: probabilistic querying, neural sequence models, recurrent neural networks

TL;DR: By casting general probabilistic queries in the right framework, we can effectively use importance sample and/or beam search to efficiently and accurately estimate arbitrary queries for black-box sequence models.

Abstract: In reasoning about sequential events it is natural to pose probabilistic queries such as “when will event A occur next” or “what is the probability of A occurring before B”, with applications in areas such as user modeling, language models, medicine, and finance. These types of queries are complex to answer compared to next-event prediction, particularly for neural autoregressive models such as recurrent neural networks and transformers. This is in part due to the fact that future querying involves marginalization over large path spaces, which is not straightforward to do efficiently in such models. In this paper we introduce a general typology for predictive queries in neural autoregressive sequence models and show that such queries can be systematically represented by sets of elementary building blocks. We leverage this typology to develop new query estimation methods based on beam search, importance sampling, and hybrids. Across four large-scale sequence datasets from different application domains, as well as for the GPT-2 language model, we demonstrate the ability to make query answering tractable for arbitrary queries in exponentially-large predictive path-spaces, and find clear differences in cost-accuracy tradeoffs between search and sampling methods.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2210.06464/code)