More specifically, I study the output layer of deep neural networks that have a large number of outputs, e.g. Large Language Models (LLMs). Any such output layer that has more outputs than inputs unavoidably has outputs that are impossible to predict. I call such outputs unargmaxable. My research identifies unargmaxable outputs in LLMs and Clinical NLP models and proposes replacement layers which guarantee that outputs of interest can be predicted.

More generally, I have a soft spot for language and structure and I enjoy creating interactive visualisations of geometric representations I am learning about; here are some examples.

Alongside my PhD, I have also been a part-time Research Assistant on Information Extraction from news articles and clinical text under the supervision of Beatrice Alex.

Selected publications

Low-Rank Softmax Can Have Unargmaxable Classes in Theory but Rarely in Practice. Andreas Grivas, Nikolay Bogoychev and Adam Lopez. ACL 2022 (Oral Presentation)
See also: , Poster, Interactive visualisation
Not a cute stroke: Analysis of Rule-and Neural Network-based Information Extraction Systems for Brain Radiology Reports. Andreas Grivas, Beatrice Alex, Claire Grover, Richard Tobin and William Whiteley. LOUHI 2020
See also:
What do Character-level Models Learn About Morphology? The Case of Dependency Parsing. Clara Vania, Andreas Grivas, and Adam Lopez. EMNLP 2018


  • My vimrc and other dotfiles can be found here.
  • My public key is 24A721BB42D9A790