Popular GitHub Repositories for Trustworthiness

[ICML 2024] TrustLLM: Trustworthiness in Large Language Models

[CVPR'25] RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness

Survey of Small Language Models from Penn State, ...

A toolbox for benchmarking trustworthiness of multimodal large language models (MultiTrust, NeurIPS 2024 Track Datasets and Benchmarks)

This is an open-source tool to assess and improve the trustworthiness of AI systems.

Codes and datasets for the paper Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse

A toolbox for benchmarking Multimodal LLM Agents trustworthiness across truthfulness, controllability, safety and privacy dimensions through 34 interactive tasks

Open-source test generation SDK for LLM applications. Access curated test sets. Build context-specific test sets and collaborate with subject matter experts.

In the dynamic landscape of medical artificial intelligence, this study explores the vulnerabilities of the Pathology Language-Image Pretraining (PLIP) model, a Vision Language Foundation model, under targeted attacks like PGD adversarial attack.

A Cross-Platform Application using TensorFlow-Lite and Flutter to recognize labels on fruits or vegetables to get more information about their trustworthiness.

Codes and Datasets for our WSDM 2022 Paper: "MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs"

A modular and extendable framework built to for the purpose of testing trustworthiness in AI language models.

Proposal of a novel adversarial attack approach, called Target Adversarial Attack against Multimedia Recommender Systems (TAaMR), to investigate the modification of MR behavior when the images of a category of low recommended products (e.g., socks) are perturbed to misclassify the deep neural classifier towards the class of more recommended products (e.g., running shoes) with human-level slight images alterations.

In today's digital landscape, misinformation can spread rapidly, making it crucial to validate online sources. This repository aims to empower users with an AI Agents-powered tool to assess the trustworthiness of information found on the web.

In this work, we provide 24 combinations of attack/defense strategies, and visual-based recommenders to 1) access performance alteration on recommendation and 2) empirically verify the effect on final users through offline visual metrics.

[NeurIPS'24] CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models