A curated list of awesome responsible machine learning resources.

Safe RLHF: Constrained Value Alignment via Safe Reinforcement Learning from Human Feedback

Secrets of RLHF in Large Language Models Part I: PPO

UQLM: Uncertainty Quantification for Language Models, is a Python package for UQ-based LLM hallucination detection

Deliver safe & effective language models

Open Source LLM toolkit to build trustworthy LLM applications. TigerArmor (AI safety), TigerRAG (embedding, RAG), TigerTune (fine-tuning)

PromptInject is a framework that assembles prompts in a modular fashion to provide a quantitative analysis of the robustness of LLMs to adversarial prompt attacks. 🏆 Best Paper Awards @ NeurIPS ML Safety Workshop 2022

Aligning AI With Shared Human Values (ICLR 2021)

[NeurIPS '23 Spotlight] Thought Cloning: Learning to Think while Acting by Imitating Human Thinking

RuLES: a benchmark for evaluating rule-following in language models

[AAAI 2025 oral] Official repository of Imitate Before Detect: Aligning Machine Stylistic Preference for Machine-Revised Text Detection

LangFair is a Python library for conducting use-case level LLM bias and fairness assessments

An unrestricted attack based on diffusion models that can achieve both good transferability and imperceptibility.

Code accompanying the paper Pretraining Language Models with Human Preferences

📚 A curated list of papers & technical articles on AI Quality & Safety

Toolkits to create a human-in-the-loop approval layer to monitor and guide AI agents workflow in real-time.

How to Make Safe AI? Let's Discuss! 💡|💬|🙌|📚

Attack to induce LLMs within hallucinations

BeaverTails is a collection of datasets designed to facilitate research on safety alignment in large language models (LLMs).

[ICLR'24 Spotlight] A language model (LM)-based emulation framework for identifying the risks of LM agents with tool use