DecodingTrust

Paid APISelf-hosted

What DecodingTrust is

DecodingTrust is a free, open-source project for assessing trustworthiness in GPT models. It gives researchers and practitioners a dataset and evaluation scripts covering toxicity, stereotype and bias, adversarial robustness, out-of-distribution robustness, privacy, adversarial demonstrations, machine ethics, and fairness. Evaluations mainly target GPT-3.5 and GPT-4, especially timestamped GPT-3.5-turbo-0301 and GPT-4-0314 versions, while causal language models hosted on Hugging Face or run locally are also supported. The repository organizes scripts by trustworthiness area and documents installation through an editable pip setup after cloning. Docker and Singularity or Apptainer containers are available, including for HPC use, and ppc64le on IBM Power-9 is supported. The project is offered through API and self-hosted deployment, under CC BY-SA 4.0. Its data includes model outputs that may be offensive.

Who it suits

It suits researchers and practitioners evaluating language-model capabilities, limitations, safety, and deployment risks. Users comfortable with local or containerized setups can run the supplied scripts and dataset.

What it does well

Covers eight trustworthiness assessment areas
Provides dataset and evaluation scripts
Supports GPT-3.5, GPT-4, and causal LLMs
Documents Docker and HPC-oriented containers
Free under CC BY-SA 4.0

What to know first

Main benchmark focus is older GPT model versions
Data may contain offensive model outputs
Installation includes PyTorch and CUDA compatibility concerns

Verdict

DecodingTrust is a focused toolkit for structured LLM trustworthiness evaluations. Its broad assessment areas and reproducibility-oriented model versions are useful, but setup and content warnings matter.

Facts from DecodingTrust's own pages

Assessment areas: The benchmark covers toxicity, stereotype and bias, adversarial robustness, out-of-distribution robustness, privacy, adversarial demonstrations, machine ethics, and fairness. · decodingtrust.github.io, 11 Oct 2026
Compute requirements: The installation instructions note that the pip installation includes PyTorch and caution users to check CUDA compatibility with the PyTorch pip wheel. · github.com, 11 Oct 2026
Container option: The repository provides Docker instructions and a Docker image for running DecodingTrust. · github.com, 11 Oct 2026
Content warning: The project warns that its data contains model outputs that may be considered offensive. · decodingtrust.github.io, 11 Oct 2026
Dataset: The dataset is distributed under the CC BY-SA 4.0 license. · decodingtrust.github.io, 11 Oct 2026
Deployment options: The project documents Docker and Singularity or Apptainer container usage, including for HPC environments. · github.com, 4 Oct 2026
Evaluation areas: The benchmark covers toxicity, stereotype and bias, adversarial robustness, out-of-distribution robustness, privacy, adversarial demonstrations, machine ethics, and fairness. · decodingtrust.github.io, 4 Oct 2026
Evaluation scripts: The project provides evaluation scripts in subdirectories for its trustworthiness facets. · decodingtrust.github.io, 11 Oct 2026
Installation: The repository recommends cloning the project and installing it in editable mode with pip; it also describes standalone package installation. · github.com, 11 Oct 2026
Intended users: The project describes its resources as helping researchers and practitioners assess language model capabilities, limitations, and risks. · decodingtrust.github.io, 11 Oct 2026
License: The dataset and project are distributed under the CC BY-SA 4.0 license. · decodingtrust.github.io, 4 Oct 2026
Model coverage limit: The repository says its benchmark mainly focuses on GPT-3.5-turbo-0301 and GPT-4-0314 for consistent conclusions and results. · github.com, 4 Oct 2026
Models: The project says its evaluations mainly focus on GPT-3.5 and GPT-4, and it also supports causal LLMs hosted on Hugging Face or locally. · github.com, 4 Oct 2026
Models evaluated: The project says its evaluation mainly focuses on GPT-3.5 and GPT-4. · decodingtrust.github.io, 11 Oct 2026
Ongoing contributions: The team invites users to submit model generations and corresponding scores through GitHub pull requests and says the dataset is expected to evolve. · decodingtrust.github.io, 11 Oct 2026
Platform support: The repository says it supports the ppc64le architecture on IBM Power-9 platforms. · github.com, 11 Oct 2026
Purpose: DecodingTrust is a research project for assessing trustworthiness in GPT models and helping researchers and practitioners understand their capabilities, limitations, and deployment risks. · decodingtrust.github.io, 11 Oct 2026
Reproducibility: The benchmark uses timestamped GPT-3.5 and GPT-4 model versions to support consistent results and reproducibility. · github.com, 4 Oct 2026
Resources: The project provides a dataset and evaluation scripts organized by trustworthiness area. · decodingtrust.github.io, 4 Oct 2026
Support: The project invites questions at [email protected]. · decodingtrust.github.io, 11 Oct 2026
Supported architecture: The repository says it supports the ppc64le architecture on IBM Power-9 platforms. · github.com, 4 Oct 2026

Where DecodingTrust stands on Freedom251

LLM Evaluation Tools#25 of 29