Recovering Lesion Parameters from Aphasic Picture Naming Error Profiles in Large Language Models
Summary
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.
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# Recovering Lesion Parameters from Aphasic Picture Naming Error Profiles in Large Language Models Source: [https://arxiv.org/abs/2608.06429](https://arxiv.org/abs/2608.06429) Authors:[Yong Yang](https://arxiv.org/search/cs?searchtype=author&query=Yang,+Y),[Roger Newman\-Norlund](https://arxiv.org/search/cs?searchtype=author&query=Newman-Norlund,+R),[Xiang Guan](https://arxiv.org/search/cs?searchtype=author&query=Guan,+X),[Saeed Ahmadi](https://arxiv.org/search/cs?searchtype=author&query=Ahmadi,+S),[Regan Willis](https://arxiv.org/search/cs?searchtype=author&query=Willis,+R),[Nadra Salman](https://arxiv.org/search/cs?searchtype=author&query=Salman,+N),[Kalil Warren](https://arxiv.org/search/cs?searchtype=author&query=Warren,+K),[Sophie Arheix\-Parras](https://arxiv.org/search/cs?searchtype=author&query=Arheix-Parras,+S),[Srihari Nelakuditi](https://arxiv.org/search/cs?searchtype=author&query=Nelakuditi,+S),[Leonardo Bonilha](https://arxiv.org/search/cs?searchtype=author&query=Bonilha,+L),[Christopher Rorden](https://arxiv.org/search/cs?searchtype=author&query=Rorden,+C),[Rutvik H\. Desai](https://arxiv.org/search/cs?searchtype=author&query=Desai,+R+H),[Julius Fridriksson](https://arxiv.org/search/cs?searchtype=author&query=Fridriksson,+J) [View PDF](https://arxiv.org/pdf/2608.06429) > Abstract:Interpretability methods for large language models \(LLMs\) describe internal state but do not directly test whether that state is causally sufficient to produce the observed behavior\. In earlier work, we lesioned LLMs to produce error profiles in picture naming, a central task for assessing aphasia, and found that specific lesions produced errors resembling those of individual stroke survivors\. Here we ask the inverse question: given an error profile, can the lesion parameters that produced it be recovered, and what does this inverse problem reveal about transformer computation? Lesions in LLaVA\-Vicuna 13B were parameterized by layer index, modification percentage, and noise sigma across 4,840 configurations, and error profiles were characterized by a seven\-category clinical taxonomy \(correct, semantic, unrelated, formal, mixed, neologism, no\-response\)\. We trained a multi\-task neural network to map error profiles back to perturbation parameters\. The problem admitted a partial solution: across 10 independently trained inverse models, modification percentage and noise sigma were recoverable, whereas layer index was recoverable only within a neighborhood\. In counterfactual validation, a fresh model instance perturbed with the recovered parameters reproduced the target behavior in 81\.4% of cases\. This dissociation between low layer recovery and high counterfactual fidelity is consistent with functional redundancy across transformer layers, a property not captured by standard interpretability methods\. As an out\-of\-distribution test, we applied the trained model to picture\-naming error profiles from 278 stroke survivors; recovered parameters were syndrome\-discriminative, most strongly for perturbation intensity, indicating generalization beyond the training distribution\. Counterfactual validation provides a general framework for LLM interpretability claims beyond inverse mapping\. ## Submission history From: Yong Yang \[[view email](https://arxiv.org/show-email/382f819a/2608.06429)\] **\[v1\]**Wed, 5 Aug 2026 20:58:49 UTC \(2,874 KB\)
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