# Tracking priming-induced language recovery in aphasia with pre-trained language models

## TL;DR

This study uses pre-trained language models to track language recovery in aphasia patients after treatment, showing promising results in measuring and classifying language improvement.

## Contribution

The novel use of PLM-derived surprisals to quantify treatment-induced language recovery in aphasia, along with a prompting-based pipeline for clinical classification.

## Key findings

- Surprisal scores decreased following structural priming treatment, especially in participants with severe sentence production impairments.
- A prompting-based pipeline achieved high performance in classifying aphasia sentence correctness (F1 = 0.967) and detecting error categories (accuracy = 0.846).

## Abstract

This study explores the use of pre-trained language models (PLMs) in tracking priming treatment induced language recovery in aphasia. We evaluate PLM-derived surprisals, the negative log-probabilities of a word or a sequence of words calculated by a PLM given its preceding context, as a continuous and interpretable measure of treatment-induced language change. We found that surprisal scores decreased following structural priming treatment, especially in participants with more severe sentence production impairments. We also introduce a prompting-based pipeline for clinical classification tasks. It achieved promising results in classifying aphasia sentence correctness (F1 = 0.967) and detecting error categories in aphasia (accuracy = 0.846). Such use of PLMs for modeling, tracking, and automatically classifying language recovery in aphasia represents a promising deployment of GenAI in a clinical rehabilitation setting. Together, our PLM-based analyses offer a practical approach for modeling language rehabilitation, tracking not only language structure but also individual change over time in clinical contexts.

Identifier NTC05415501.