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

Alexis Roger, Daniel Z Kaplan, Prateek Humane, Kshitij Gupta, Qi Sun, George Adamopoulos, Jonathan Siu Chi Lim, Quentin Gregory Anthony, Edwin Fennell, Irina Rish

Keywords: Vision-Language Models, Benchmarks, Scalling Suites

TL;DR: We trained a suite of models in order to analyze the robustness of current benchmarks over scale and created a benchmark to fill a major gap we identified.

Abstract: The proliferation of Vision-Language Models (VLMs) in the past several years calls for rigorous and comprehensive evaluation methods and benchmarks. 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.

Primary Area: datasets and benchmarks

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Submission Number: 3218