7 papers
Towards Understanding and Measuring COGNITIVE ATROPHY in LLM Behaviour
Abeer Badawi, Moyosoreoluwa Olatosi, Negin Baghbanzadeh +5
Recent incidents involving LLMs used for mental-health support reveal a critical evaluation gap: surface-level safety scores do not capture how models behave across realistic, emot…
OpenMedReason: Scientific Reasoning Supervision for Medical Vision-Language Models
Negin Baghbanzadeh, Pritam Sarkar, Michael Colacci +6
High-stakes clinical use of large vision-language models (LVLMs) requires reasoning that is grounded in visual evidence and clinical knowledge, not just correct final answers. We i…
Fine-Grained Benchmark Generation for Comprehensive Evaluation of Foundation Models
Mohammed Saidul Islam, Negin Baghbanzadeh, Farnaz Kohankhaki +5
Evaluation of foundation models often rely on aggregate scores from benchmarks that lack comprehensive coverage and metadata for a fine-grained evaluation. We introduce a framework…
When Does RL Help Medical VLMs? Disentangling Vision, SFT, and RL Gains
Ahmadreza Jeddi, Kimia Shaban, Negin Baghbanzadeh +4
Reinforcement learning (RL) is increasingly used to post-train medical Vision-Language Models (VLMs), yet it remains unclear whether RL improves medical visual reasoning or mainly…
Open-PMC-18M: A High-Fidelity Large Scale Medical Dataset for Multimodal Representation Learning
Negin Baghbanzadeh, Mohammed Saidul Islam, Sajad Ashkezari +2
In biomedical vision-language modeling, datasets are typically mined from scientific literature, pairing compound figures with captions that are short, context-dependent, and ofter…
Advancing Medical Representation Learning Through High-Quality Data
Negin Baghbanzadeh, Adibvafa Fallahpour, Yasaman Parhizkar +8
Despite the growing scale of medical Vision-Language datasets, the impact of dataset quality on model performance remains under-explored. We introduce Open-PMC, a high-quality medi…