arXiv AI

A Monosemantic Attribution Framework for Stable Interpretability in Clinical Neuroscience Transformer-Based Language Models

arXiv:2601. 17952v2 Announce Type: replace-cross Abstract: Interpretability remains a key challenge for deploying language models (LM) in clinical settings such as progression diagnosis of Alzheimer disease, where early and trustworthy predictions are essential.

arXiv AI

Sep 1

A Unifying Perspective on Language Model Representations: From Filler-Role Structure to Mechanistic Interpretability

arXiv:2608.29034v1 Announce Type: cross Abstract: A wide range of methods have been proposed for interpreting language models, delivering important insights into their inner workings. However, differ...

By Zhang Enyan, R. Thomas McCoy

llms safety

arXiv AI

Jul 8

X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models

arXiv:2607. 06163v1 Announce Type: cross Abstract: Foundation Models for Electronic Health Records (FEMRs) are pretrained on large-scale structured patient data, enabling them to convert longitudinal patient trajectories into generalizable representations for diverse clinical prediction tasks.

By Jie Huang, Pengfei Yin, Zihan Xu, Daniel Capurro, Mike Conway, Ting Dang

llms safety

arXiv AI

Jul 13

iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis

arXiv:2607. 08778v1 Announce Type: cross Abstract: Alzheimer's Disease (AD) is a complex neurodegenerative disorder that continues to impact millions of people worldwide.

By Farica Zhuang, Seong Woo Han, Zixuan Wen, Shu Yang, Yize Zhao, Li Shen

llms safety

arXiv AI

Jun 29

From Black-Box to Clinical Insight: A Multi-Stage Explainable Framework for Speech-Based Cognitive Impairment Detection

arXiv:2606. 27973v1 Announce Type: cross Abstract: Speech-based cognitive impairment detection offers a noninvasive, accessible alternative to costly biomarker assays, yet transformer-based models remain clinically uninterpretable.

By Yasaman Haghbin, Sina Rashidi, Ali Zolnour, Fatemeh Taherinezhad, Ali Fartoot, Hossein Azadmaleki, James M Noble, Maryam Dadkhah, Maryam Zolnoori

llms multimodal benchmarks safety

arXiv Machine Learning

Jul 2

Explainability in mulimodal deep transformation models for stroke outcome prediction

arXiv:2504. 06299v2 Announce Type: replace-cross Abstract: Multimodal prediction models based on imaging and clinical data are increasingly used for clinical decision support, yet their interpretability remains limited.

By Lisa Herzog, Jonas Br\"andli, Maurice Schneeberger, Loran Avci, Nordin Dari, Martin H\"ansel, Hakim Baazaoui, Pascal B\"uhler, Susanne Wegener, Beate Sick

diffusion multimodal benchmarks safety

arXiv Machine Learning

Aug 21

Explainable Transformer Models for Clinical Prediction Tasks on Structured Electronic Health Records

arXiv:2608. 20315v1 Announce Type: new Abstract: Predictive models over structured electronic health records (EHRs) remain central to machine learning for healthcare, but few have jointly emphasized quantitative laboratory information and interpretability with respect to input medical events.

By Jun Ni Du, Lukas Adamek, Maxim Kryukov, Flavio Dormont, Ziv Bar-Joseph, Sven Jager, Brandon Rufino

llms fine-tuning benchmarks safety

arXiv AI

Jun 30

NEURON: A Neuro-symbolic System for Grounded Clinical Explainability

arXiv:2605. 01189v2 Announce Type: replace Abstract: Clinical AI adoption is hindered by the black-box/grey-box nature of high-performing models, which lack the ontological grounding and narrative transparency required for professional-level explainability.

By Anuradha Chandrasekaran, Dimitrios Zikos, Mutlu Mete, Alan Pang, Brady D. Lund, Kewei Sha

llms rag safety

arXiv AI

Jul 1

Listening Between the Lines: Joint Learning of ASR Embeddings and LLM-Augmented Linguistics for Dementia Detection

arXiv:2606. 30675v1 Announce Type: cross Abstract: Early detection of dementia through speech analysis offers a non-invasive screening alternative, but capturing both acoustic and linguistic biomarkers remains challenging.

By Olivier Jiyoun Jung, Jonghyeon Park, Myungwoo Oh

llms rag multimodal benchmarks

arXiv Computation and Language

Sep 15

A Hybrid Hierarchical 1D-CNN-BiLSTM Framework for Extractive Summarization of Biomedical and Clinical Text

arXiv:2609.13481v1 Announce Type: new Abstract: Large language models have made abstractive summarization remarkably fluent, but generated summaries can hallucinate facts, posing serious risks in bio...

By Saad Bin Ather, Muhammad Saif, Ali Hassan Khan, Manzer Abbas, Hajra Waheed

llms rag nlp

arXiv AI

Aug 7

Explanations of Large Language Models Explain Language Representations in the Brain

arXiv:2502. 14671v4 Announce Type: replace-cross Abstract: Large Language Model (LLM) representations are known to align with brain activity during language processing, but it remains unclear what drives this alignment.

By Maryam Rahimi, Mohammad Reza Daliri, Yadollah Yaghoobzadeh

llms safety

arXiv Machine Learning

Sep 15

SPICE: Simple Polysemantic Feature Interpretation via Clustering-based Explanation

arXiv:2609.13198v1 Announce Type: new Abstract: One of the pivotal recent challenges in neural network interpretability is polysemanticity, where a single neuron is activated by multiple, often unrel...

By Sehyun Lee, Dahee Kwon, Damin Lee, Jaesik Choi

llms safety

arXiv AI

Aug 3

A Human-Centered Validation of the Explainability-Performance Coefficient

arXiv:2607. 29614v1 Announce Type: cross Abstract: The rapid adoption of deep learning models in high-risk domains has intensified the need for trustworthy Explainable Artificial Intelligence (XAI).

By Christian Oliva, Luis F. Lago-Fern\'andez

efficiency

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