--- title: "AlignLLMHumanSurvey vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/garyyufei-alignllmhumansurvey-vs-wangrongsheng-awesome-llm-resources" tools: ["garyyufei-alignllmhumansurvey", "wangrongsheng-awesome-llm-resources"] --- # AlignLLMHumanSurvey vs awesome-LLM-resources *GraphCanon updated Aug 17, 2026* ## Verdict Pick AlignLLMHumanSurvey if 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; pick awesome-LLM-resources if awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL. [AlignLLMHumanSurvey](https://arxiv.org/abs/2307.12966) reports 742 GitHub stars, 30 forks, and 0 open issues, last pushed Sep 11, 2023. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 8.8k stars, 950 forks, and 23 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [AlignLLMHumanSurvey's repository](https://github.com/GaryYufei/AlignLLMHumanSurvey) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [AlignLLMHumanSurvey](/tools/garyyufei-alignllmhumansurvey.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | A survey on aligning large language models with human expectations | Summary of the world's best LLM resources. | | Stars | 742 | 8,845 | | Forks | 30 | 950 | | Open issues | 0 | 23 | | Language | - | - | | Adopt for | 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 | awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Evaluation & Observability, Model Training | AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [AlignLLMHumanSurvey](/tools/garyyufei-alignllmhumansurvey.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1060d | 2d | | Open issues (now) | 0 | 23 | | Stars delta | Unknown | +142 (30d) | | Open issues delta | Unknown | -13 (30d) | | Full report | [trust report](/tools/garyyufei-alignllmhumansurvey/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## Decision facts: AlignLLMHumanSurvey - **Adopt for:** 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 ## Decision facts: awesome-LLM-resources - **Adopt for:** awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a ## Choose when ### Choose AlignLLMHumanSurvey if… - Tags unique to AlignLLMHumanSurvey: awesome, chatgpt, chinese-llama, gpt-4. - 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。 - Leaner open-issue backlog (0). ### Choose awesome-LLM-resources if… - Tags unique to awesome-LLM-resources: awesome-list, book, course, llm. - Also covers AI Agents, Developer Tools, Inference & Serving, LLM Frameworks. - - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ## When NOT to use AlignLLMHumanSurvey - ，AlignLLMHumanSurvey，，。 ## When NOT to use awesome-LLM-resources - - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ## Common questions ### What is the difference between AlignLLMHumanSurvey and awesome-LLM-resources? AlignLLMHumanSurvey: A survey on aligning large language models with human expectations. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories. ### When should I choose AlignLLMHumanSurvey over awesome-LLM-resources? Choose AlignLLMHumanSurvey over awesome-LLM-resources when Tags unique to AlignLLMHumanSurvey: awesome, chatgpt, chinese-llama, gpt-4; 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。; Leaner open-issue backlog (0). ### When should I choose awesome-LLM-resources over AlignLLMHumanSurvey? Choose awesome-LLM-resources over AlignLLMHumanSurvey when Tags unique to awesome-LLM-resources: awesome-list, book, course, llm; Also covers AI Agents, Developer Tools, Inference & Serving, LLM Frameworks; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ### When should I avoid AlignLLMHumanSurvey? ，AlignLLMHumanSurvey，，。 ### When should I avoid awesome-LLM-resources? - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ### Is AlignLLMHumanSurvey or awesome-LLM-resources more popular on GitHub? awesome-LLM-resources has more GitHub stars (8,845 vs 742). Stars measure visibility, not whether either tool fits your constraints. ### Are AlignLLMHumanSurvey and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to AlignLLMHumanSurvey or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [AlignLLMHumanSurvey alternatives](/tools/garyyufei-alignllmhumansurvey/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([AlignLLMHumanSurvey markdown twin](/tools/garyyufei-alignllmhumansurvey/alternatives.md), [awesome-LLM-resources markdown twin](/tools/wangrongsheng-awesome-llm-resources/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/garyyufei-alignllmhumansurvey-vs-wangrongsheng-awesome-llm-resources.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, AlignLLMHumanSurvey or awesome-LLM-resources? AlignLLMHumanSurvey: Dormant. awesome-LLM-resources: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for AlignLLMHumanSurvey and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AlignLLMHumanSurvey trust report](/tools/garyyufei-alignllmhumansurvey/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=garyyufei-alignllmhumansurvey`](/api/graphcanon/graph?tool=garyyufei-alignllmhumansurvey) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_