Alexis Roger

CHIRP: A Fine-Grained Benchmark for Open-Ended Response Evaluation in Vision-Language Models

Alexis Roger

Prateek Humane

Daniel Z Kaplan

Kshitij Gupta

Qirui Sun

George Adamopoulos

Jonathan Siu Chi Lim

Quentin Gregory Anthony

Edwin Fennell

Irina Rish

The proliferation of Vision-Language Models (VLMs) in the past several years calls for rigorous and comprehensive evaluation methods and ben… (voir plus)chmarks. This work analyzes existing VLM evaluation techniques, including automated metrics, AI-based assessments, and human evaluations across diverse tasks. We first introduce Robin - a novel suite of VLMs that we built by combining Large Language Models (LLMs) and Vision Encoders (VEs) at multiple scales, and use Robin to identify shortcomings of current evaluation approaches across scales. Next, to overcome the identified limitations, we introduce CHIRP - a new long form response benchmark we developed for more robust and complete VLM evaluation. We provide open access to the Robin training code, model suite, and CHIRP benchmark to promote reproducibility and advance VLM research.

2025-01-16

ArXiv (prépublication)

arxiv.org

Robin: a Suite of Multi-Scale Vision-Language Models and the CHIRP Evaluation Benchmark

Alexis Roger

Prateek Humane

Daniel Z Kaplan

Kshitij Gupta

Qirui Sun

George Adamopoulos

Jonathan Siu Chi Lim

Quentin Gregory Anthony

Edwin Fennell

Irina Rish

The proliferation of Vision-Language Models (VLMs) in the past several years calls for rigorous and comprehensive evaluation methods and ben… (voir plus)chmarks. This work analyzes existing VLM evaluation techniques, including automated metrics, AI-based assessments, and human evaluations across diverse tasks. We first introduce Robin - a novel suite of VLMs that we built by combining Large Language Models (LLMs) and Vision Encoders (VEs) at multiple scales, and use Robin to identify shortcomings of current evaluation approaches across scales. Next, to overcome the identified limitations, we introduce CHIRP - a new long form response benchmark we developed for more robust and complete VLM evaluation. We provide open access to the Robin training code, model suite, and CHIRP benchmark to promote reproducibility and advance VLM research.

2025-01-16

ArXiv (prépublication)

doi.org

arxiv.org

The Effect of Data Corruption on Multimodal Long Form Responses

Daniel Z Kaplan

Alexis Roger

Mohamed Osman

Irina Rish

Despite significant progress, Vision-Language Models (VLMs) still struggle with hallucinations, especially in long-form responses. Existing … (voir plus)strategies have had limited successes in specific cases, and long-form generation remains problematic. In this work we attempt to establish the link between the data used to train the model and the hallucinations in the model's output. To this end, we examine hallucinations through data corruption. We develop a method to corrupt training data and then train models with this data to see the effect on performance. We will show that corrupting only a small portion of the long-form training data significantly impairs the performance of the model on long-form tasks, while leaving simpler tasks like visual question-answering and multiple choice relatively intact. All training code and models are released for reproducibility and future research.

2024-07-03

ICML.cc/2024/Workshop/FM-Wild (poster)

openreview.net

Towards Adversarially Robust Vision-Language Models: Insights from Design Choices and Prompt Formatting Techniques

Daniel Z Kaplan

Vision-Language Models (VLMs) have witnessed a surge in both research and real-world applications. However, as they becoming increasingly pr… (voir plus)evalent, ensuring their robustness against adversarial attacks is paramount. This work systematically investigates the impact of model design choices on the adversarial robustness of VLMs against image-based attacks. Additionally, we introduce novel, cost-effective approaches to enhance robustness through prompt formatting. By rephrasing questions and suggesting potential adversarial perturbations, we demonstrate substantial improvements in model robustness against strong image-based attacks such as Auto-PGD. Our findings provide important guidelines for developing more robust VLMs, particularly for deployment in safety-critical environments.

2024-06-28

ICML.cc/2024/Workshop/NextGenAISafety (poster)

doi.org

openreview.net

Towards ethical multimodal systems

Alexis Roger

Esma Aimeur

Irina Rish

2023-04-26

ArXiv (prépublication)

doi.org

arxiv.org