# Extracting Rules from Neural Networks with Partial Interpretations

arXiv: https://arxiv.org/abs/2204.00360 · HTML: https://mlsanity.com/paper/2204.00360/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 1 Apr 2022.

## Builds on (most cited first, 3 of 3)

- [A Survey on Neural Network Interpretability](https://mlsanity.com/paper/2012.14261/) (2020)
- [Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples](https://mlsanity.com/paper/1711.09576/) (2017)
- [Weighted Automata Extraction from Recurrent Neural Networks via Regression on State Spaces](https://mlsanity.com/paper/1904.02931/) (2019)

## Built on by (most cited first, 1 of 1)

- [Learning Horn Envelopes via Queries from Large Language Models](https://mlsanity.com/paper/2305.12143/) (2023)

## Abstract (arXiv)

We investigate the problem of extracting rules, expressed in Horn logic, from neural network models. Our work is based on the exact learning model, in which a learner interacts with a teacher (the neural network model) via queries in order to learn an abstract target concept, which in our case is a set of Horn rules. We consider partial interpretations to formulate the queries. These can be understood as a representation of the world where part of the knowledge regarding the truthiness of propositions is unknown. We employ Angluin s algorithm for learning Horn rules via queries and evaluate our strategy empirically.