Awesome-Align-LLM-Human

A collection of papers and resources about aligning large language models (LLMs) with human.

Large Language Models (LLMs) trained on extensive textual corpora have emerged as leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite their notable performance, these models are prone to certain limitations such as misunderstanding human instructions, generating potentially biased content, or factually incorrect (hallucinated) information. Hence, aligning LLMs with human expectations has become an active area of interest within the research community. This survey presents a comprehensive overview of these alignment technologies, including the following aspects. (1) Data collection (2) Training methodologies (3) Model Evaluation. In conclusion, we collate and distill our findings, shedding light on several promising future research avenues in the field. This survey, therefore, serves as a valuable resource for anyone invested in understanding and advancing the alignment of LLMs to better suit human-oriented tasks and expectations.

We hope this repository can help researchers and practitioners to get a better understanding of this emerging field. If this repository is helpful for you, please help us by citing this paper:

@article{aligning_llm_human,
    title={Aligning Large Language Models with Human: A Survey},
    author={Yufei Wang and Wanjun Zhong and Liangyou Li and Fei Mi and Xingshan Zeng and Wenyong Huang and Lifeng Shang and Xin Jiang and Qun Liu},
    journal={arXiv preprint arXiv:2307.12966},
    year={2023}
}

News

🔭 This project is under development. You can hit the STAR and WATCH to follow the updates.

• 2023/07/31: Our survey paper is put into [Podcast @ papersread.ai]
• 2023/07/25: Our initial survey paper Aligning Large Language Models with Human: A Survey becomes available.

Table of Contents

• News
• Awesome-Aligning-LLM-Human
  • Related Surveys
  • Alignment Data
    • Data From Human
    • Data From Strong LLMs
    • Instructions Management
  • Alignment Training
    • Online Human Alignment
    • Offline Human Alignment
    • Parameter-Efficient Training
  • Alignment Evaluation
    • Evaluation Design Principles
    • Evaluation Benchmarks
    • Evaluation Paradigms
  • Alignment Toolkits

Related Surveys

• A Survey of Large Language Models [Paper]
• A Survey on Multimodal Large Language Models [Paper]
• A Survey on Evaluation of Large Language Models [Paper]
• Challenges and Applications of Large Language Models [Paper]
• Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond [Paper]
• Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey [Paper]
• A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation [Paper]
• Unifying Large Language Models and Knowledge Graphs: A Roadmap [Paper]
• Tool Learning with Foundation Models [Paper]
• Eight Things to Know about Large Language Models [Paper]
• Open Problems and Fundamen