4 citations · 6 across the 12 of their papers we have counts for
7 papers · 1 filter
Steering Large Language Models to Evaluate and Amplify Creativity
Matthew Lyle Olson, Neale Ratzlaff, Musashi Hinck +2
Although capable of generating creative text, Large Language Models (LLMs) are poor judges of what constitutes "creativity". In this work, we show that we can leverage this knowled…
Debias your Large Multi-Modal Model at Test-Time via Non-Contrastive Visual Attribute Steering
Neale Ratzlaff, Matthew Lyle Olson, Musashi Hinck +4
Large Multi-Modal Models (LMMs) have demonstrated impressive capabilities as general-purpose chatbots able to engage in conversations about visual inputs. However, their responses…
Debiasing Large Vision-Language Models by Ablating Protected Attribute Representations
Neale Ratzlaff, Matthew Lyle Olson, Musashi Hinck +3
Large Vision Language Models (LVLMs) such as LLaVA have demonstrated impressive capabilities as general-purpose chatbots that can engage in conversations about a provided input ima…
ClimDetect: A Benchmark Dataset for Climate Change Detection and Attribution
Sungduk Yu, Brian L. White, Anahita Bhiwandiwalla +6
Detecting and attributing temperature increases driven by climate change is crucial for understanding global warming and informing adaptation strategies. However, distinguishing hu…
Why do LLaVA Vision-Language Models Reply to Images in English?
Musashi Hinck, Carolin Holtermann, Matthew Lyle Olson +6
We uncover a surprising multilingual bias occurring in a popular class of multimodal vision-language models (VLMs). Including an image in the query to a LLaVA-style VLM significant…
LVLM-Interpret: An Interpretability Tool for Large Vision-Language Models
Gabriela Ben Melech Stan, Estelle Aflalo, Raanan Yehezkel Rohekar +7
In the rapidly evolving landscape of artificial intelligence, multi-modal large language models are emerging as a significant area of interest. These models, which combine various…
LLaVA-Gemma: Accelerating Multimodal Foundation Models with a Compact Language Model
Musashi Hinck, Matthew L. Olson, David Cobbley +2
We train a suite of multimodal foundation models (MMFM) using the popular LLaVA framework with the recently released Gemma family of large language models (LLMs). Of particular int…