Projects sharing features with SEED Bench

Chenhang Cui, Yiyang Zhou, Xinyu Yang, Shirley Wu, Linjun Zhang, James Zou, Huaxiu Yao *Equal Contribution

Official Repo of "MMBench: Is Your Multi-modal Model an All-around Player?"

ECCV 2024 M3DBench introduces a comprehensive 3D instruction-following dataset with support for interleaved multi-modal prompts.

MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities (ICML 2024)

Data and code for paper "M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models"

Code for the paper Benchmarking Large Multimodal Models against Common Corruptions.

NeurIPS 2023 Datasets and Benchmarks Track LAMM: Multi-Modal Large Language Models and Applications as AI Agents

VLMEvalKit is a vision-language model evaluation framework and inference engine designed to run standardized benchmarks and measure model accuracy across diverse visual datasets. It serves as a multimodal model benchmark and performance toolkit for calculating metrics and comparing model responses. The toolkit includes a specialized visual reasoning evaluator that uses adversarial samples to distinguish actual image understanding from reliance on language patterns. It also provides capabilities for image generation evaluation, testing a model's ability to create or modify visuals based on tex

This project is a technical curriculum and development guide focused on large language model prompt engineering, fine-tuning, and the creation of retrieval augmented generation applications. It serves as a comprehensive resource for developers to master crafting precise instructions and textual patterns to improve the quality and predictability of model outputs. The material covers the end-to-end workflow of adapting open-source models to specific datasets and integrating language models with vector databases to generate responses based on private information. It also provides a systematic ap

FaithScore: Fine-grained Evaluations of Hallucinations in Large Vision-Language Models

🤗 Dataset | 📖 arXiv | GitHub Atsuyuki Miyai 1   Jingkang Yang 2   Jingyang Zhang 3   Yifei Ming 4   Qing Yu 1,5   Go Irie 6   Sharon Yixuan Li 4   Hai Li 3   Ziwei Liu 2 Kiyoharu Aizawa 1 1 The University of Tokyo  2 S-Lab, Nanyang Technological…

This repository is no longer maintained and deprecated in favour of the repository at https://github.com/dsbowen/strong_reject. Please refer to that repository for full paper replication including human evaluation details and using the fine-tuned version of the StrongReject evaluator.

This is the official repository of our paper ScanQA: 3D Question Answering for Spatial Scene Understanding (CVPR 2022) by Daichi Azuma, Taiki Miyanishi, Shuhei Kurita, and Motoki Kawanabe. We propose a new 3D spatial understanding task for 3D question answering (3D-QA). In the 3D-QA task, models…

🍎 Project Page 📖 arXiv Paper 📊 Dataset🏆 Leaderboard

This repo provides the source code & data of our paper: Evaluating Object Hallucination in Large Vision-Language Models (EMNLP 2023).

An automatic evaluation framework for Multimodal Chain-of-Thought.

Open-source red teaming framework for MLLMs with 42+ attack methods

The jailbreak-evaluation is an easy-to-use Python package for language model jailbreak evaluation. The jailbreak-evaluation is designed for comprehensive and accurate evaluation of language model jailbreak attempts. Currently, jailbreak-evaluation support evaluating a language model jailbreak…

We introduce the task of dense captioning in 3D scans from commodity RGB-D sensors. As input, we assume a point cloud of a 3D scene; the expected output is the bounding boxes along with the descriptions for the underlying objects. To address the 3D object detection and description problems, we…

Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructions

Paper | Github | Dataset | Model

🌐 Homepage | 🤗 Paper | 📖 arXiv | 🤗 Dataset | 🏆 EvalAI | GitHub