1 citations · 2 across the 18 of their papers we have counts for
15 papers · 1 filter
SAT: Dynamic Spatial Aptitude Training for Multimodal Language Models
Arijit Ray, Jiafei Duan, Ellis Brown +9
Reasoning about motion and space is a fundamental cognitive capability that is required by multiple real-world applications. While many studies highlight that large multimodal lang…
SIMS-V: Simulated Instruction-Tuning for Spatial Video Understanding
Ellis Brown, Arijit Ray, Ranjay Krishna +3
Despite impressive high-level video comprehension, multimodal language models struggle with spatial reasoning across time and space. While current spatial training approaches rely…
LATTE: Learning to Think with Vision Specialists
Zixian Ma, Jianguo Zhang, Zhiwei Liu +9
While open-source vision-language models perform well on simple question-answering, they still struggle with complex questions that require both perceptual and reasoning capabiliti…
Multilingual Diversity Improves Vision-Language Representations
Thao Nguyen, Matthew Wallingford, Sebastin Santy +5
Massive web-crawled image-text datasets lay the foundation for recent progress in multimodal learning. These datasets are designed with the goal of training a model to do well on s…
Rethinking Human Preference Evaluation of LLM Rationales
Ziang Li, Manasi Ganti, Zixian Ma +3
Large language models (LLMs) often generate natural language rationales -- free-form explanations that help improve performance on complex reasoning tasks and enhance interpretabil…
MedBLINK: Probing Basic Perception in Multimodal Language Models for Medicine
Mahtab Bigverdi, Wisdom Ikezogwo, Kevin Zhang +5
Multimodal language models (MLMs) show promise for clinical decision support and diagnostic reasoning, raising the prospect of end-to-end automated medical image interpretation. Ho…