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

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

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

Aligning AI With Shared Human Values (ICLR 2021)

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

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

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

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

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

🦀 Prevents outdated Rust code suggestions from AI assistants. This MCP server fetches current crate docs, uses embeddings/LLMs, and provides accurate context via a tool call.

📚 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.

Code accompanying the paper Pretraining Language Models with Human Preferences

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

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

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