MEGA-Bench: A Complete AI Benchmark that Scales Multimodal Analysis to Over 500 Actual-World Duties at a Manageable Inference Value

A serious problem within the analysis of vision-language fashions (VLMs) lies in understanding their numerous capabilities throughout a variety of real-world duties. Current benchmarks usually fall brief, specializing in slender units of duties or restricted output codecs, leading to insufficient analysis of the fashions’ full potential. The issue turns into extra pronounced when evaluating newer multimodal basis fashions that want complete testing throughout quite a few software domains. These fashions require a benchmarking suite able to evaluating their talents in varied enter and output situations whereas minimizing inference prices.

A crew of researchers from the MEGA-Bench Crew introduces MEGA-Bench, an modern and complete benchmark that scales multimodal analysis to embody greater than 500 real-world duties. MEGA-Bench goals to offer a high-quality, systematic analysis of multimodal fashions throughout varied inputs, outputs, and talent necessities, protecting a broader vary of use circumstances than earlier benchmarks. In contrast to earlier benchmarks targeted on standardized outputs like multiple-choice questions, MEGA-Bench embraces a large variety of outputs, corresponding to numbers, phrases, code, LaTeX, and JSON. This enables for an correct evaluation of generative and predictive capabilities, bringing forth the finer particulars of mannequin efficiency.

The construction of MEGA-Bench is meticulously designed to make sure complete protection. It comprises 505 multimodal duties, which have been curated and annotated by 16 knowledgeable contributors. The benchmark taxonomy contains classes like software kind, enter kind, output format, and talent necessities, making certain numerous and complete job protection. To accommodate the number of outputs, over 40 metrics have been developed, offering fine-grained and multidimensional evaluation of the fashions’ capabilities. The benchmark additionally introduces an interactive visualization device for customers, enabling them to discover mannequin strengths and weaknesses throughout completely different dimensions, making MEGA-Bench a extra sensible analysis device in comparison with conventional benchmarks.

The outcomes from making use of MEGA-Bench to numerous state-of-the-art VLMs highlighted some key findings. Amongst flagship fashions, GPT-4o outperformed others, together with Claude 3.5, with a 3.5% larger rating. Amongst open-sourced fashions, Qwen2-VL achieved top-tier efficiency, practically matching proprietary fashions and outperforming the second-best open-source mannequin by roughly 10%. For effectivity fashions, Gemini 1.5 Flash was discovered to be the best total, with a particular power in duties associated to Person Interfaces and Paperwork. One other perception was that proprietary fashions benefited from Chain-of-Thought prompting, whereas open-source fashions struggled to leverage it successfully.

In conclusion, MEGA-Bench represents a big development in multimodal benchmarking, providing an intensive and fine-grained analysis of the capabilities of vision-language fashions. By supporting numerous inputs and outputs, in addition to detailed efficiency metrics, it offers a extra practical analysis of how these fashions carry out throughout real-world duties. This benchmark permits builders and researchers to higher perceive and optimize VLMs for sensible functions, setting a brand new customary for multimodal mannequin analysis.

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