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arXiv · 2511.20507

The Text Aphasia Battery (TAB): A Clinically-Grounded Benchmark for Aphasia-Like Deficits in Language Models

Nathan Roll·Jill Kries·Flora Jin·Catherine Wang·Ann Marie Finley·Meghan Sumner·Cory Shain·Laura Gwilliams
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Abstract

Large language models (LLMs) have emerged as a candidate "model organism" for human language, offering an unprecedented opportunity to study the computational basis of linguistic disorders like aphasia. However, traditional clinical assessments are ill-suited for LLMs, as they presuppose human-like pragmatic pressures and probe cognitive processes not inherent to artificial architectures. We introduce the Text Aphasia Battery (TAB), a text-only benchmark adapted from the Quick Aphasia Battery (QAB) to assess aphasic-like deficits in LLMs. The TAB comprises four subtests: Connected Text, Word Comprehension, Sentence Comprehension, and Repetition. This paper details the TAB's design, subtests, and scoring criteria. To facilitate large-scale use, we validate an automated evaluation protocol using Gemini 2.5 Flash, which achieves reliability comparable to expert human raters (prevalence-weighted Cohen's kappa = 0.255 for model--consensus agreement vs. 0.286 for human--human agreement). We release TAB as a clinically-grounded, scalable framework for analyzing language deficits in artificial systems.

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Nathan Roll, Jill Kries, Flora Jin, Catherine Wang, Ann Marie Finley, Meghan Sumner, Cory Shain, Laura Gwilliams. 2025-11-25. The Text Aphasia Battery (TAB): A Clinically-Grounded Benchmark for Aphasia-Like Deficits in Language Models. https://arxiv.org/abs/2511.20507

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