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Evaluating Multimodal Language Models

Published on:

4 October 2023

Primary Category:

Computation and Language

Paper Authors:

Utsav Garg,

Erhan Bas


Key Details

Compares 5 major multimodal LLMs on diverse benchmarks

Shows importance of large vision encoders and decoder fine-tuning

Demonstrates data diversity is key, model size plateaus

Analyzes tradeoffs of different model components

Provides guidance for training robust multimodal LLMs

AI generated summary

Evaluating Multimodal Language Models

This paper benchmarks publicly available multimodal language models on tasks like visual question answering, captioning, and classification. Through experiments, it reveals insights on optimal model components and training strategies.

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