LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models
Authors: name: Kaichen Zhang, name: Bo Li, name: Peiyuan Zhang, name: Fanyi Pu, name: Joshua Adrian Cahyono, name: Kairui Hu, name: Shuai Liu, name: Yuanhan Zhang, name: Jingkang Yang, name: Chunyuan Li, name: Ziwei Liu

The advances of large foundation models necessitate wide-coverage, low-cost,
and zero-contamination benchmarks. Despite continuous exploration of language
model evaluations, comprehensive studies on the evaluation of Large Multi-modal
Models (LMMs) remain limited. In this work, we introduce LMMS-EVAL, a unified
and standardized multimodal benchmark framework with over 50 tasks and more
than 10 models to promote transparent and reproducible evaluations. Although
LMMS-EVAL offers comprehensive coverage, we find it still falls short in
achieving low cost and zero contamination. To approach this evaluation
trilemma, we further introduce LMMS-EVAL LITE, a pruned evaluation toolkit that
emphasizes both coverage and efficiency. Additionally, we present Multimodal
LIVEBENCH that utilizes continuously updating news and online forums to assess
models' generalization abilities in the wild, featuring a low-cost and
zero-contamination evaluation approach. In summary, our work highlights the
importance of considering the evaluation trilemma and provides practical
solutions to navigate the trade-offs in evaluating large multi-modal models,
paving the way for more effective and reliable benchmarking of LMMs. We
opensource our codebase and maintain leaderboard of LIVEBENCH at
https://github.com/EvolvingLMMs-Lab/lmms-eval and
https://huggingface.co/spaces/lmms-lab/LiveBench.