--- title: "Awesome-LLMs-ICLR-24 vs Awesome-Multimodal-Large-Language-Models" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/azminewasi-awesome-llms-iclr-24-vs-bradyfu-awesome-multimodal-large-language-models" tools: ["azminewasi-awesome-llms-iclr-24", "bradyfu-awesome-multimodal-large-language-models"] --- # Awesome-LLMs-ICLR-24 vs Awesome-Multimodal-Large-Language-Models *GraphCanon updated Aug 17, 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-Multimodal-Large-Language-Models if awesome-Multimodal-Large-Language-Models is a curated collection of surveys and benchmarks focused on multimodal large language models (MLLMs), encompassing evaluation frameworks, interactive Omni MLLMs, and benchmarking. [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-Multimodal-Large-Language-Models](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models) has 18k stars, 1.1k forks, and 111 open issues, last pushed Aug 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-Multimodal-Large-Language-Models's repository](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models). | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [Awesome-Multimodal-Large-Language-Models](/tools/bradyfu-awesome-multimodal-large-language-models.md) | | --- | --- | --- | | Tagline | Compilation of LLM papers from ICLR 2024 | Latest Advances on Multimodal Large Language Models | | Stars | 72 | 17,978 | | Forks | 5 | 1,133 | | Open issues | 0 | 111 | | 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-Multimodal-Large-Language-Models is a curated collection of surveys and benchmarks focused on multimodal large language models (MLLMs), encompassing evaluation frameworks, interactive Omni MLLMs, and benchmarking | | Persona | - | - | | Runtime | - | - | | License | MIT | - | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [Awesome-Multimodal-Large-Language-Models](/tools/bradyfu-awesome-multimodal-large-language-models.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 856d | 2d | | Open issues (now) | 0 | 111 | | Stars delta | Unknown | +29 (30d) | | Open issues delta | Unknown | +4 (30d) | | Full report | [trust report](/tools/azminewasi-awesome-llms-iclr-24/trust.md) | [trust report](/tools/bradyfu-awesome-multimodal-large-language-models/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-Multimodal-Large-Language-Models - **Adopt for:** Awesome-Multimodal-Large-Language-Models is a curated collection of surveys and benchmarks focused on multimodal large language models (MLLMs), encompassing evaluation frameworks, interactive Omni MLLMs, and benchmarking ## 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, Model Training. - 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-Multimodal-Large-Language-Models if… - Tags unique to Awesome-Multimodal-Large-Language-Models: chain-of-thought, in-context-learning, instruction-following, instruction-tuning. - - You need comprehensive resources for evaluating multimodal LLMs and want access to the latest research findings in this area. - More GitHub stars (18k 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 Awesome-Multimodal-Large-Language-Models - - If your primary focus is on single-modality language models, without a need to integrate visual or audio elements. - - If you prefer tools that provide hands-on implementation guidance rather than surveys and benchmarks for theoretical exploration. ## Common questions ### What is the difference between Awesome-LLMs-ICLR-24 and Awesome-Multimodal-Large-Language-Models? Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. Awesome-Multimodal-Large-Language-Models: Latest Advances on Multimodal Large Language Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLMs-ICLR-24 over Awesome-Multimodal-Large-Language-Models? Choose Awesome-LLMs-ICLR-24 over Awesome-Multimodal-Large-Language-Models when Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework; Also covers Developer Tools, Inference & Serving, Model Training; 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-Multimodal-Large-Language-Models over Awesome-LLMs-ICLR-24? Choose Awesome-Multimodal-Large-Language-Models over Awesome-LLMs-ICLR-24 when Tags unique to Awesome-Multimodal-Large-Language-Models: chain-of-thought, in-context-learning, instruction-following, instruction-tuning; - You need comprehensive resources for evaluating multimodal LLMs and want access to the latest research findings in this area; More GitHub stars (18k 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 Awesome-Multimodal-Large-Language-Models? - If your primary focus is on single-modality language models, without a need to integrate visual or audio elements. - If you prefer tools that provide hands-on implementation guidance rather than surveys and benchmarks for theoretical exploration. ### Is Awesome-LLMs-ICLR-24 or Awesome-Multimodal-Large-Language-Models more popular on GitHub? Awesome-Multimodal-Large-Language-Models has more GitHub stars (17,978 vs 72). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMs-ICLR-24 and Awesome-Multimodal-Large-Language-Models open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Awesome-LLMs-ICLR-24 or Awesome-Multimodal-Large-Language-Models? GraphCanon lists graph-backed alternatives at [Awesome-LLMs-ICLR-24 alternatives](/tools/azminewasi-awesome-llms-iclr-24/alternatives) and [Awesome-Multimodal-Large-Language-Models alternatives](/tools/bradyfu-awesome-multimodal-large-language-models/alternatives) ([Awesome-LLMs-ICLR-24 markdown twin](/tools/azminewasi-awesome-llms-iclr-24/alternatives.md), [Awesome-Multimodal-Large-Language-Models markdown twin](/tools/bradyfu-awesome-multimodal-large-language-models/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-bradyfu-awesome-multimodal-large-language-models.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-Multimodal-Large-Language-Models? Awesome-LLMs-ICLR-24: Dormant. Awesome-Multimodal-Large-Language-Models: 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-Multimodal-Large-Language-Models? 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-Multimodal-Large-Language-Models trust report](/tools/bradyfu-awesome-multimodal-large-language-models/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/_