Summary of Vibe-eval: a Hard Evaluation Suite For Measuring Progress Of Multimodal Language Models, by Piotr Padlewski et al.

Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models

by Piotr Padlewski, Max Bain, Matthew Henderson, Zhongkai Zhu, Nishant Relan, Hai Pham, Donovan Ong, Kaloyan Aleksiev, Aitor Ormazabal, Samuel Phua, Ethan Yeo, Eugenie Lamprecht, Qi Liu, Yuqi Wang, Eric Chen, Deyu Fu, Lei Li, Che Zheng, Cyprien de Masson d’Autume, Dani Yogatama, Mikel Artetxe, Yi Tay

First submitted to arxiv on: 3 May 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The proposed Vibe-Eval framework and benchmark aim to evaluate multimodal chat models by presenting them with 269 visual understanding prompts, including 100 of hard difficulty. This open-ended evaluation assesses both day-to-day tasks and the capabilities of frontier models, with over half of the hard prompts being answered incorrectly by existing models. The authors explore design and evaluation nuances, discussing trade-offs between human and automatic evaluation. They also demonstrate the correlation between Reka Core’s automatic model evaluation and human judgment.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Vibe-Eval is a new way to test chatbots that can understand pictures. It gives them 269 tricky questions to answer, with some really hard ones. The goal is to see how well they do on everyday tasks and on super-hard challenges. Most of the time, these chatbots get the easy questions right but struggle with the tough ones. This helps us figure out what makes a good chatbot and how we can improve them.