--- title: "Awesome-LLMs-ICLR-24 vs best_AI_papers_2022" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/azminewasi-awesome-llms-iclr-24-vs-louisfb01-best-ai-papers-2022" tools: ["azminewasi-awesome-llms-iclr-24", "louisfb01-best-ai-papers-2022"] --- # Awesome-LLMs-ICLR-24 vs best_AI_papers_2022 *GraphCanon updated Aug 8, 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 best_AI_papers_2022 if best AI Papers from 2022 offers video explanations and code links for selected research papers. [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. [best_AI_papers_2022](https://www.louisbouchard.ai) has 3.2k stars, 197 forks, and 0 open issues, last pushed Oct 18, 2023. Figures are from public GitHub metadata via [Awesome-LLMs-ICLR-24's repository](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) and [best_AI_papers_2022's repository](https://github.com/louisfb01/best_AI_papers_2022). | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [best_AI_papers_2022](/tools/louisfb01-best-ai-papers-2022.md) | | --- | --- | --- | | Tagline | Compilation of LLM papers from ICLR 2024 | A curated list of breakthrough AI papers from 2022 with video explanations and code links | | Stars | 72 | 3,187 | | Forks | 5 | 197 | | Open issues | 0 | 0 | | 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. | Best AI Papers from 2022 offers video explanations and code links for selected research papers. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Evaluation & Observability, 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) | [best_AI_papers_2022](/tools/louisfb01-best-ai-papers-2022.md) | | --- | --- | --- | | Days since push | 856d | 1016d | | Full report | [trust report](/tools/azminewasi-awesome-llms-iclr-24/trust.md) | [trust report](/tools/louisfb01-best-ai-papers-2022/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: best_AI_papers_2022 - **Adopt for:** Best AI Papers from 2022 offers video explanations and code links for selected research papers. ## Choose when ### 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, Inference & Serving, LLM Frameworks. - 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 best_AI_papers_2022 if… - Tags unique to best_AI_papers_2022: ai, computer-vision, deep-learning, machine-learning. - Need to catch up on key innovations in AI from 2022 - More GitHub stars (3.2k vs 72) - visibility, not fit. ## 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 best_AI_papers_2022 - Looking for real-time updates or post-2022 research findings - Require detailed technical analysis beyond paper abstracts ## Common questions ### What is the difference between Awesome-LLMs-ICLR-24 and best_AI_papers_2022? Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. best_AI_papers_2022: A curated list of breakthrough AI papers from 2022 with video explanations and code links. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLMs-ICLR-24 over best_AI_papers_2022? Choose Awesome-LLMs-ICLR-24 over best_AI_papers_2022 when Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework; Also covers Developer Tools, Inference & Serving, LLM Frameworks; 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 best_AI_papers_2022 over Awesome-LLMs-ICLR-24? Choose best_AI_papers_2022 over Awesome-LLMs-ICLR-24 when Tags unique to best_AI_papers_2022: ai, computer-vision, deep-learning, machine-learning; Need to catch up on key innovations in AI from 2022; More GitHub stars (3.2k vs 72) - visibility, not fit. ### 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 best_AI_papers_2022? Looking for real-time updates or post-2022 research findings Require detailed technical analysis beyond paper abstracts ### Is Awesome-LLMs-ICLR-24 or best_AI_papers_2022 more popular on GitHub? best_AI_papers_2022 has more GitHub stars (3,187 vs 72). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMs-ICLR-24 and best_AI_papers_2022 open source? Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, best_AI_papers_2022: MIT). ### Where can I find alternatives to Awesome-LLMs-ICLR-24 or best_AI_papers_2022? GraphCanon lists graph-backed alternatives at [Awesome-LLMs-ICLR-24 alternatives](/tools/azminewasi-awesome-llms-iclr-24/alternatives) and [best_AI_papers_2022 alternatives](/tools/louisfb01-best-ai-papers-2022/alternatives) ([Awesome-LLMs-ICLR-24 markdown twin](/tools/azminewasi-awesome-llms-iclr-24/alternatives.md), [best_AI_papers_2022 markdown twin](/tools/louisfb01-best-ai-papers-2022/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-louisfb01-best-ai-papers-2022.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 best_AI_papers_2022? Awesome-LLMs-ICLR-24: Dormant. best_AI_papers_2022: 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 Awesome-LLMs-ICLR-24 and best_AI_papers_2022? 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); [best_AI_papers_2022 trust report](/tools/louisfb01-best-ai-papers-2022/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/_