**Macaw** is a generative question-answering (QA) system that is built on UnifiedQA, itself built on [T5](https://paperswithcode.com/method/t5). Macaw has three interesting features. First, it often produces high-quality answers to questions far outside the domain it was trained on, sometimes surprisingly so. Second, Macaw allows different permutations (“an gles”) of inputs and outputs to be used. For example, we can give it a question and get an answer; or give it an answer and get a question; or give it a question and answer and get a set of multiple-choice (MC) options for that question. This multi-angle QA capability allows versatility in the way Macaw can be used, include recursively using outputs as new inputs to the system. Finally, Macaw also generates explanations as an optional output (or even input) element.

Symbolic rule learning methods find regularities in data that can be expressed in the form of 'if-then' rules based on symbolic representations of the data.

Symbolic rule learning

Scalable and interpretable rule-based link prediction for large heterogeneous knowledge graphs

Macaw

General-Purpose Question-Answering with Macaw

The Lion optimizer is discovered by symbolic program search. It is more memory-efficient than most adaptive optimizers as it only needs to momentum. The update of Lion is produced by the sign function.

Source	General-Purpose Question-Answering with Macaw
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com