{"data":{"slug":"garyyufei-alignllmhumansurvey","name":"AlignLLMHumanSurvey","tagline":"A survey on aligning large language models with human expectations","github_url":"https://github.com/GaryYufei/AlignLLMHumanSurvey","owner":"GaryYufei","repo":"AlignLLMHumanSurvey","owner_avatar_url":"https://avatars.githubusercontent.com/u/8288965?v=4","primary_language":null,"stars":742,"forks":30,"topics":["awesome","chatgpt","chinese-llama","gpt-4","large-language-models","llama","llama2","llms","rlhf","supervised-finetuning","survey"],"archived":false,"github_pushed_at":"2023-09-11T03:38:05+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/garyyufei-alignllmhumansurvey","markdown_url":"https://www.graphcanon.com/tools/garyyufei-alignllmhumansurvey.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/garyyufei-alignllmhumansurvey","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=garyyufei-alignllmhumansurvey","description":"Aligning Large Language Models with Human: A Survey","homepage_url":"https://arxiv.org/abs/2307.12966","license":null,"open_issues":0,"watchers":26,"ai_summary":"This repository provides a comprehensive overview of technologies for aligning LLMs with human values, focusing on data collection, training methodologies, and model evaluation.","readme_excerpt":"# Awesome-Align-LLM-Human\n\nA collection of papers and resources about aligning large language models (LLMs) with human.\n\nLarge 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.\n\nWe 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:\n```bash\n@article{aligning_llm_human,\n    title={Aligning Large Language Models with Human: A Survey},\n    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},\n    journal={arXiv preprint arXiv:2307.12966},\n    year={2023}\n}\n```\n## News\n🔭 This project is under development. You can hit the **STAR** and **WATCH** to follow the updates.\n- 2023/07/31: Our survey paper is put into [[Podcast @ papersread.ai]](https://papersread.ai/e/aligning-large-language-models-with-human-a-survey/)\n- 2023/07/25: Our initial survey paper [Aligning Large Language Models with Human: A Survey](arxiv.org/abs/2307.12966) becomes available.\n\n## Table of Contents\n- [News](#news)\n- [Awesome-Aligning-LLM-Human](#awesome-align-llm-human)\n    - [Related Surveys](#related-surveys)\n    - [Alignment Data](#alignment-data)\n        - [Data From Human](#data-from-human)\n        - [Data From Strong LLMs](#data-from-strong-llms)\n        - [Instructions Management](#instructions-management)\n    - [Alignment Training](#alignment-training)\n        - [Online Human Alignment](#online-human-alignment)\n        - [Offline Human Alignment](#offline-human-alignment)\n        - [Parameter-Efficient Training](#parameter-efficient-training)\n    - [Alignment Evaluation](#alignment-evaluation)\n        - [Evaluation Design Principles](#evaluation-design-principles) \n        - [Evaluation Benchmarks](#evaluation-benchmarks)\n        - [Evaluation Paradigms](#evaluation-paradigms)\n    - [Alignment Toolkits](#alignment-toolkits)\n\n## Related Surveys\n- A Survey of Large Language Models [[Paper]](https://arxiv.org/abs/2303.18223)\n- A Survey on Multimodal Large Language Models [[Paper]](https://arxiv.org/abs/2306.13549)\n- A Survey on Evaluation of Large Language Models [[Paper]](https://arxiv.org/abs/2307.03109)\n- Challenges and Applications of Large Language Models [[Paper]](https://arxiv.org/abs/2307.10169)\n- Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond [[Paper]](https://arxiv.org/abs/2304.13712)\n- Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey [[Paper]](https://arxiv.org/abs/2305.18703)\n- A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation [[Paper]](https://arxiv.org/abs/2305.11391)\n- Unifying Large Language Models and Knowledge Graphs: A Roadmap [[Paper]](https://arxiv.org/abs/2306.08302)\n- Tool Learning with Foundation Models [[Paper]](https://arxiv.org/abs/2304.08354)\n- Eight Things to Know about Large Language Models [[Paper]](https://arxiv.org/abs/2304.00612)\n- Open Problems and Fundamen","github_created_at":"2023-07-23T06:41:56+00:00","created_at":"2026-07-11T10:32:32.825295+00:00","updated_at":"2026-07-12T05:13:09.619389+00:00","categories":[{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"},{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"}],"tags":[{"slug":"chinese-llama","name":"chinese-llama"},{"slug":"awesome","name":"awesome"},{"slug":"llms","name":"llms"},{"slug":"llama","name":"llama"},{"slug":"large-language-models","name":"large-language-models"},{"slug":"gpt-4","name":"gpt-4"},{"slug":"chatgpt","name":"chatgpt"},{"slug":"llama2","name":"llama2"}],"trust":{"provenance":{"is_fork":false,"github_id":669704171,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-11T10:32:33.474Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":1034,"last_release_at":null},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T10:32:34.511Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-11T11:25:12.308Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["当您需要全面了解将大型语言模型与人类期望对齐的技术和方法时，可以使用AlignLLMHumanSurvey，它涵盖了数据收集、训练方法和模型评估等多个方面。","如果您是研究人员或从业者，并且希望深入了解这一新兴领域的发展动态和未来研究方向，这个调查提供了宝贵的资源。"],"when_not_to_use":["当您需要具体的技术实现代码或工具箱时，请勿使用AlignLLMHumanSurvey，因为它仅仅是一个汇总知识的调研文档，不提供具体的编程实现细节。","如果您正在寻找特定模型的性能评估数据或者准备在实际项目中直接应用某种对齐技术，则这个资源可能不会满足您的需求，因为它更侧重于理论和调查研究。"],"source":"enrich:decision_facts","observed_at":"2026-07-11T11:25:42.630Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"AlignLLMHumanSurvey is a survey repository aggregating resources and research on aligning large language models with human expectations through various methodologies like data collection, training techniques, and model评价"}]}}