This repository provides a comprehensive survey of research and resources focused on aligning Large Language Models (LLMs) with human expectations and instructions. It targets researchers and practitioners in NLP and AI, offering a structured overview of data collection, training methodologies, and evaluation techniques for LLM alignment, aiming to foster advancements in creating more helpful, harmless, and honest AI systems.

How It Works

The survey categorizes alignment efforts into three main pillars: Data Collection (human-generated, LLM-generated, instruction management), Training Methodologies (online/offline human alignment, parameter-efficient tuning), and Model Evaluation (design principles, benchmarks, paradigms). This structured approach allows for a systematic understanding of the diverse techniques employed to bridge the gap between LLM capabilities and human intent.

Quick Start & Requirements

• No installation or execution commands are provided as this is a survey paper and resource collection.
• Requirements are conceptual, covering understanding of LLMs, NLP, and AI alignment research.
• Links to the survey paper and related resources are available within the README.

Highlighted Details

• Extensive categorization of alignment data sources, including human feedback, LLM-generated instructions, and domain-specific datasets.
• Detailed breakdown of training methods, from Reinforcement Learning from Human Feedback (RLHF) variants to parameter-efficient fine-tuning (PEFT) techniques like LoRA and QLoRA.
• Comprehensive overview of evaluation benchmarks and paradigms, covering general knowledge, reasoning, code generation, safety, and long-context understanding.
• Includes a curated list of related surveys and alignment toolkits, such as Llama V1/V2, Colossal-AI, and FastChat.

Maintenance & Community

• The project is under active development, with news updates indicating recent additions and mentions of the survey paper.
• No specific community channels (Discord, Slack) or social media handles are listed.

Licensing & Compatibility

• The repository itself does not appear to have a specific license mentioned for the curated content.
• Individual papers and code repositories linked within the survey will have their own licenses.

Limitations & Caveats

• The project is a survey and a collection of links, not a runnable codebase.
• As a rapidly evolving field, the survey may not capture the absolute latest advancements immediately upon publication.