#Human Feedback

The project demonstrates the implementation of Reinforcement Learning with Human Feedback (RLHF) on the PaLM infrastructure, enabling researchers to explore open-source systems similar to ChatGPT. It provides guidelines on using the PaLM framework, training reward models with human input, and integrating RLHF for improved performance. The contributions of CarperAI and support from Hugging Face are acknowledged, as well as potential enhancements like Direct Preference Optimization.

Examine how human preferences are incorporated into language model pretraining via Hugging Face Transformers. This method uses annotations and feedback to align models with human values, enhancing their ability to reduce toxicity and meet compliance standards. Learn about methods, configurations, and available pretrained models for tasks including toxicity management, PII detection, and PEP8 adherence, documented using wandb. Leverage this codebase to refine models for better processing of language aligned with human expectations.

This framework enables research into learning from human feedback using methods like RLHF, supporting feedback simulation and automated evaluations. It offers reference implementations for developers and researchers, facilitating research into instruction-aligned models. The framework is compatible with multiple language models, including GPT-4, and focuses on simulation accuracy for improved model evaluation and development.

This repository provides crucial datasets for AI safety research, including human preference data on the helpfulness and harmlessness of language models and red teaming data designed to mitigate harmful effects. The insights from the data utilize human feedback to inform safer model training methodologies, featuring detailed JSONL files with paired preference texts and comprehensive records of adversarial interactions. The datasets serve researchers focused on model behavior and AI ethics, addressing concerns like discriminatory language and self-harm. Engage with datasets drawn from key studies to propel advanced research into AI safety and performance.