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llms-benchmarking

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RaptorMai / CompBench

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CompBench evaluates the comparative reasoning of multimodal large language models (MLLMs) with 40K image pairs and questions across 8 dimensions of relative comparison: visual attribute, existence, state, emotion, temporality, spatiality, quantity, and quality. CompBench covers diverse visual domains, including animals, fashion, sports, and scenes.

benchmark reasoning vision-and-language multimodal-deep-learning human-annotation foundation-models large-language-models llms vision-language-model multimodal-large-language-models evaluation-llms llms-benchmarking
  • Updated Aug 6, 2024
  • Jupyter Notebook

nachoDRT / MERIT-Dataset

Star 9

The MERIT Dataset is a fully synthetic, labeled dataset created for training and benchmarking LLMs on Visually Rich Document Understanding tasks. It is also designed to help detect biases and improve interpretability in LLMs, where we are actively working. This repository is actively maintained, and new features are continuously being added.

biases synthetic-dataset-generation layoutlm synthetic-dataset layoutxlm token-classification layoutlmv3 layoutlmv2 llms-benchmarking
  • Updated Sep 6, 2024
  • Python

SuperBruceJia / Awesome-Mixture-of-Experts

Star 8

Awesome Mixture of Experts (MoE): A Curated List of Mixture of Experts (MoE) and Mixture of Multimodal Experts (MoME)

artificial-intelligence sparse moe load-balancing multimodal-learning mixture-of-experts mome gating-network foundation-models large-language-models llms large-language-model large-vision-language-models expert-network llms-reasoning llms-benchmarking mixtrure-of-multimodal-experts sparse-moe sparse-mixture-of-experts sparse-mixture-of-multimodal-experts
  • Updated Sep 25, 2024

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