A fundamental question in interpretability research is to what extent neural networks, particularly language models, implement reusable functions through subnetworks that can be composed to perform more complex tasks.Recent advances in mechanistic interpretability have made progress in identifying circuits, which represent the minimal computational subgraphs responsible for a model's behavior on specific tasks.However, most studies focus on identifying circuits for individual tasks without investigating how functionally similar circuits relate to each other.To address this gap, we study the modularity of neural networks by analyzing circuits for highly compositional subtasks within a transformer-based language model.Specifically, given a probabilistic context-free grammar, we identify and compare circuits responsible for ten modular string-edit operations.Our results indicate that functionally similar circuits exhibit both notable node overlap and crosstask faithfulness.Moreover, we demonstrate that the circuits identified can be reused and combined through set operations to represent more complex functional model capabilities.

Existing methods for complexity estimation are typically developed for entire documents. This limitation in scope makes them inapplicable for shorter pieces of text, such as health assessment tools. These typically consist of lists of independent sentences, all of which are too short for existing methods to apply. The choice of wording in these assessment tools is crucial, as both the cognitive capacity and the linguistic competency of the intended patient groups could vary substantially. As a first step towards creating better tools for supporting health practitioners, we develop a two-step approach for estimating lexical complexity that does not rely on any pre-annotated data. We implement our approach for the Norwegian language and verify its effectiveness using statistical testing and a qualitative evaluation of samples from real assessment tools. We also investigate the relationship between our complexity measure and certain features typically associated with complexity in the literature, such as word length, frequency, and the number of syllables.