5 citations · 8 across the 8 of their papers we have counts for
8 papers
LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model Compression
Souvik Kundu, Anahita Bhiwandiwalla, Sungduk Yu +6
Despite recent efforts in understanding the compression impact on large language models (LLMs) in terms of their downstream task performance and trustworthiness on relatively simpl…
FiVL: A Framework for Improved Vision-Language Alignment through the Lens of Training, Evaluation and Explainability
Estelle Aflalo, Gabriela Ben Melech Stan, Tiep Le +5
Large Vision Language Models (LVLMs) have achieved significant progress in integrating visual and textual inputs for multimodal reasoning. However, a recurring challenge is ensurin…
FastRM: An efficient and automatic explainability framework for multimodal generative models
Gabriela Ben-Melech Stan, Estelle Aflalo, Man Luo +5
Large Vision Language Models (LVLMs) have demonstrated remarkable reasoning capabilities over textual and visual inputs. However, these models remain prone to generating misinforma…
Scaling Knowledge Graph Construction through Synthetic Data Generation and Distillation
Prafulla Kumar Choubey, Xin Su, Man Luo +9
Document-level knowledge graph (KG) construction faces a fundamental scaling challenge: existing methods either rely on expensive large language models (LLMs), making them economic…
SocialCounterfactuals: Probing and Mitigating Intersectional Social Biases in Vision-Language Models with Counterfactual Examples
Phillip Howard, Avinash Madasu, Tiep Le +3
While vision-language models (VLMs) have achieved remarkable performance improvements recently, there is growing evidence that these models also posses harmful biases with respect…
Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning
Xin Su, Tiep Le, Steven Bethard +1
An important open question in the use of large language models for knowledge-intensive tasks is how to effectively integrate knowledge from three sources: the model's parametric me…
COCO-Counterfactuals: Automatically Constructed Counterfactual Examples for Image-Text Pairs
Tiep Le, Vasudev Lal, Phillip Howard
Counterfactual examples have proven to be valuable in the field of natural language processing (NLP) for both evaluating and improving the robustness of language models to spurious…
Probing Intersectional Biases in Vision-Language Models with Counterfactual Examples
Phillip Howard, Avinash Madasu, Tiep Le +2
While vision-language models (VLMs) have achieved remarkable performance improvements recently, there is growing evidence that these models also posses harmful biases with respect…