Tag
This paper introduces PRISM, a perturbation-based method for spatially resolved interpretability of large language models, adapting neuroimaging subtraction analysis to transformers and applying it in parallel to post-stroke aphasia patients to recover shared phonemic-favoring dissociations.
This paper asks whether lesion parameters in LLaVA-Vicuna 13B can be recovered from aphasic picture-naming error profiles. The authors find that perturbation intensity is recoverable while layer index is only approximate, with 81.4% counterfactual fidelity and syndrome-discriminative generalization to stroke survivors, suggesting functional redundancy across transformer layers.
This study investigates whether instruction-tuned LLMs (Llama-3.1-8B, Qwen2.5-7B, Mistral-7B, Phi-3-mini) can reliably classify Correct Information Units in aphasic discourse transcripts. Few-shot prompting yields competitive F1 scores (0.776–0.817) for three models, but performance varies by severity and human agreement remains insufficient for fully autonomous use.
This paper critiques the use of single-reference ground truth in ASR evaluation, arguing it causes epistemic injustice for speakers with aphasia. It proposes a new metric, Epistemic Injustice Distance, and advocates for WER-Range to account for diverse transcription conventions.