mlsanityData to 14 Oct 2026

Building Models of Neurological Language

arXiv 2506.06208 ↗First posted 6 Jun 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

This report documents the development and evaluation of domain-specific language models for neurology. Initially focused on building a bespoke model, the project adapted to rapid advances in open-source and commercial medical LLMs, shifting toward leveraging retrieval-augmented generation (RAG) and representational models for secure, local deployment. Key contributions include the creation of neurology-specific datasets (case reports, QA sets, textbook-derived data), tools for multi-word expression extraction, and graph-based analyses of medical terminology. The project also produced scripts and Docker containers for local hosting. Performance metrics and graph community results are reported, with future possible work open for multimodal models using open-source architectures like phi-4.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 33 references that are in the map. First posted on arXiv 6 Jun 2025.

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19 of 33 references.

References · 19

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7 more
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  1. 2026Domain-Specific Knowledge Graphs in RAG-Enhanced Healthcare LLMs
  2. 2025A Multi-Agent Approach to Neurological Clinical Reasoning
  3. 2026NeuroMosaic: Anatomically Grounded Multimodal Large Language Modeling for Molecularly Aware Glioma Reasoning from 3D MRI and Clinical Narratives
  4. 2024Biomedical Large Languages Models Seem not to be Superior to Generalist Models on Unseen Medical Data
  5. 2025Leveraging Language Models and RAG for Efficient Knowledge Discovery in Clinical Environments
  6. 2025NDAI-NeuroMAP: A Neuroscience-Specific Embedding Model for Domain-Specific Retrieval
  7. 2024Bailicai: A Domain-Optimized Retrieval-Augmented Generation Framework for Medical Applications
  8. 2026NeuroVLM-Bench: Evaluation of Vision-Enabled Large Language Models for Clinical Reasoning in Neurological Disorders