--- title: "LLMSys-PaperList vs Awesome-LLMs-ICLR-24" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/amberljc-llmsys-paperlist-vs-azminewasi-awesome-llms-iclr-24" tools: ["amberljc-llmsys-paperlist", "azminewasi-awesome-llms-iclr-24"] --- # LLMSys-PaperList vs Awesome-LLMs-ICLR-24 *GraphCanon updated Aug 8, 2026* ## Verdict Pick LLMSys-PaperList if lLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems; 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. [LLMSys-PaperList](https://github.com/AmberLJC/LLMSys-PaperList) reports 2.2k GitHub stars, 120 forks, and 1 open issues, last pushed Jul 25, 2026. [Awesome-LLMs-ICLR-24](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) has 72 stars, 5 forks, and 0 open issues, last pushed Apr 4, 2024. Figures are from public GitHub metadata via [LLMSys-PaperList's repository](https://github.com/AmberLJC/LLMSys-PaperList) and [Awesome-LLMs-ICLR-24's repository](https://github.com/azminewasi/Awesome-LLMs-ICLR-24). | | [LLMSys-PaperList](/tools/amberljc-llmsys-paperlist.md) | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | | --- | --- | --- | | Tagline | Curated list of academic papers related to Large Language Model systems | Compilation of LLM papers from ICLR 2024 | | Stars | 2,220 | 72 | | Forks | 120 | 5 | | Open issues | 1 | 0 | | Language | Python | - | | Adopt for | LLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems. | 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. | | Persona | - | - | | Runtime | - | - | | License | (unknown) | MIT | | Categories | Inference & Serving, LLM Frameworks, Model Training | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMSys-PaperList](/tools/amberljc-llmsys-paperlist.md) | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 12d | 856d | | Open issues (now) | 1 | 0 | | Full report | [trust report](/tools/amberljc-llmsys-paperlist/trust.md) | [trust report](/tools/azminewasi-awesome-llms-iclr-24/trust.md) | ## Decision facts: LLMSys-PaperList - **Hosting:** unknown - (repository does not specify hosting environment) - **Adopt for:** LLMSys-PaperList offers a comprehensive list of papers and resources tailored specifically to Large Language Model (LLM) systems. - **License detail:** (unknown) ## 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. ## Choose when ### Choose LLMSys-PaperList if… - (repository does not specify hosting environment) - Tags unique to LLMSys-PaperList: academic-sources, framework-overview, inference-techniques, research papers. - - When you need a curated list focusing on technical advancements in pre-training, post-training, serving, and multi-modal LLM systems. ### Choose Awesome-LLMs-ICLR-24 if… - Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework. - Also covers Developer Tools, Evaluation & Observability. - 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 NOT to use LLMSys-PaperList - - If you are looking for a general repository of machine learning papers rather than specific developments related to Large Language Models. - - When your primary need is documentation or code examples rather than academic papers and project insights. - - For applications where real-time updates and active community support are imperative, as LLMSys-PaperList primarily serves as a static list without user interaction features like commenting or liveＱ ## 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. ## Common questions ### What is the difference between LLMSys-PaperList and Awesome-LLMs-ICLR-24? LLMSys-PaperList: Curated list of academic papers related to Large Language Model systems. Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLMSys-PaperList over Awesome-LLMs-ICLR-24? Choose LLMSys-PaperList over Awesome-LLMs-ICLR-24 when (repository does not specify hosting environment); Tags unique to LLMSys-PaperList: academic-sources, framework-overview, inference-techniques, research papers; - When you need a curated list focusing on technical advancements in pre-training, post-training, serving, and multi-modal LLM systems. ### When should I choose Awesome-LLMs-ICLR-24 over LLMSys-PaperList? Choose Awesome-LLMs-ICLR-24 over LLMSys-PaperList when Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework; Also covers Developer Tools, Evaluation & Observability; 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 avoid LLMSys-PaperList? - If you are looking for a general repository of machine learning papers rather than specific developments related to Large Language Models. - When your primary need is documentation or code examples rather than academic papers and project insights. - For applications where real-time updates and active community support are imperative, as LLMSys-PaperList primarily serves as a static list without user interaction features like commenting or liveＱ ### 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. ### Is LLMSys-PaperList or Awesome-LLMs-ICLR-24 more popular on GitHub? LLMSys-PaperList has more GitHub stars (2,220 vs 72). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMSys-PaperList and Awesome-LLMs-ICLR-24 open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to LLMSys-PaperList or Awesome-LLMs-ICLR-24? GraphCanon lists graph-backed alternatives at [LLMSys-PaperList alternatives](/tools/amberljc-llmsys-paperlist/alternatives) and [Awesome-LLMs-ICLR-24 alternatives](/tools/azminewasi-awesome-llms-iclr-24/alternatives) ([LLMSys-PaperList markdown twin](/tools/amberljc-llmsys-paperlist/alternatives.md), [Awesome-LLMs-ICLR-24 markdown twin](/tools/azminewasi-awesome-llms-iclr-24/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/amberljc-llmsys-paperlist-vs-azminewasi-awesome-llms-iclr-24.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLMSys-PaperList or Awesome-LLMs-ICLR-24? LLMSys-PaperList: Active. Awesome-LLMs-ICLR-24: Dormant. 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 LLMSys-PaperList and Awesome-LLMs-ICLR-24? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMSys-PaperList trust report](/tools/amberljc-llmsys-paperlist/trust); [Awesome-LLMs-ICLR-24 trust report](/tools/azminewasi-awesome-llms-iclr-24/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=amberljc-llmsys-paperlist`](/api/graphcanon/graph?tool=amberljc-llmsys-paperlist) - 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/_