--- title: "Awesome-LLMs-ICLR-24 vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/azminewasi-awesome-llms-iclr-24-vs-wangrongsheng-awesome-llm-resources" tools: ["azminewasi-awesome-llms-iclr-24", "wangrongsheng-awesome-llm-resources"] --- # Awesome-LLMs-ICLR-24 vs awesome-LLM-resources *GraphCanon updated Sep 20, 2026* ## Verdict Pick Awesome-LLMs-ICLR-24 if awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024; pick awesome-LLM-resources if awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. [Awesome-LLMs-ICLR-24](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) reports 72 GitHub stars, 5 forks, and 0 open issues, last pushed Apr 4, 2024. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 9.0k stars, 993 forks, and 40 open issues, last pushed Sep 14, 2026. Figures are from public GitHub metadata via [Awesome-LLMs-ICLR-24's repository](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | Compilation of LLM papers from ICLR 2024 | Summary of the world's best LLM resources. | | Stars | 72 | 8,968 | | Forks | 5 | 993 | | Open issues | 0 | 40 | | Language | - | - | | Adopt for | Awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024. | awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. | | Persona | - | - | | Runtime | - | - | | License | MIT | The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution. | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 887d | 3d | | Open issues (now) | 0 | 40 | | Stars delta | 0 (30d) | +123 (30d) | | Open issues delta | 0 (30d) | +17 (30d) | | Full report | [trust report](/tools/azminewasi-awesome-llms-iclr-24/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## Decision facts: Awesome-LLMs-ICLR-24 - **Adopt for:** Awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024. ## Decision facts: awesome-LLM-resources - **Pricing:** freemium - The repository itself is free to use, but some linked resources may require payment or have associated costs. - **Requirements:** The repository does not specify any technical requirements for accessing its content. - **Adopt for:** awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. - **License detail:** The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution. ## Choose when ### Choose Awesome-LLMs-ICLR-24 if… - License: Awesome-LLMs-ICLR-24 is MIT, awesome-LLM-resources is Apache-2.0. - Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework. - If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights. ### Choose awesome-LLM-resources if… - License: awesome-LLM-resources is Apache-2.0, Awesome-LLMs-ICLR-24 is MIT. - Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.. - Requirements: The repository does not specify any technical requirements for accessing its content.. - Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models. - Also covers AI Agents, Computer Vision, Data & Retrieval. - When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more. ## When NOT to use Awesome-LLMs-ICLR-24 - If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024. - For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis. ## When NOT to use awesome-LLM-resources - If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. - When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support. ## Common questions ### What is the difference between Awesome-LLMs-ICLR-24 and awesome-LLM-resources? Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. 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 Awesome-LLMs-ICLR-24 over awesome-LLM-resources? Choose Awesome-LLMs-ICLR-24 over awesome-LLM-resources when License: Awesome-LLMs-ICLR-24 is MIT, awesome-LLM-resources is Apache-2.0; Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework; If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights. ### When should I choose awesome-LLM-resources over Awesome-LLMs-ICLR-24? Choose awesome-LLM-resources over Awesome-LLMs-ICLR-24 when License: awesome-LLM-resources is Apache-2.0, Awesome-LLMs-ICLR-24 is MIT; Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.; Requirements: The repository does not specify any technical requirements for accessing its content.; Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models; Also covers AI Agents, Computer Vision, Data & Retrieval; When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more. ### When should I avoid Awesome-LLMs-ICLR-24? If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024. For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis. ### When should I avoid awesome-LLM-resources? If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support. ### Is Awesome-LLMs-ICLR-24 or awesome-LLM-resources more popular on GitHub? awesome-LLM-resources has more GitHub stars (8,968 vs 72). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMs-ICLR-24 and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, awesome-LLM-resources: Apache-2.0). ### Where can I find alternatives to Awesome-LLMs-ICLR-24 or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [Awesome-LLMs-ICLR-24 alternatives](/tools/azminewasi-awesome-llms-iclr-24/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([Awesome-LLMs-ICLR-24 markdown twin](/tools/azminewasi-awesome-llms-iclr-24/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/azminewasi-awesome-llms-iclr-24-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, Awesome-LLMs-ICLR-24 or awesome-LLM-resources? Awesome-LLMs-ICLR-24: 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 Awesome-LLMs-ICLR-24 and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLMs-ICLR-24 trust report](/tools/azminewasi-awesome-llms-iclr-24/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=azminewasi-awesome-llms-iclr-24`](/api/graphcanon/graph?tool=azminewasi-awesome-llms-iclr-24) - 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/_