Comparison
Awesome-LLMs-ICLR-24 vs Awesome-Multimodal-Large-Language-Models
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.
Markdown twin · Awesome-LLMs-ICLR-24 alternatives · Awesome-Multimodal-Large-Language-Models alternatives
GraphCanon updated 4d
vs
Awesome-Multimodal-Large-Language-Models
BradyFU/Awesome-Multimodal-Large-Language-Models
★ 18kpushed Aug 14, 2026
Trust & integrity
| Signal | Awesome-LLMs-ICLR-24 | Awesome-Multimodal-Large-Language-Models |
|---|---|---|
| Maintenance | Dormant (856d since push) As of 1w · github_public_v1 | Very active (2d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1w · github_public_v1 | Not a fork · Personal account As of 4d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- Awesome-LLMs-ICLR-24
- Compilation of LLM papers from ICLR 2024
- Awesome-Multimodal-Large-Language-Models
- Latest Advances on Multimodal Large Language Models
Stars
- Awesome-LLMs-ICLR-24
- 72
- Awesome-Multimodal-Large-Language-Models
- 18k
Forks
- Awesome-LLMs-ICLR-24
- 5
- Awesome-Multimodal-Large-Language-Models
- 1.1k
Open issues
- Awesome-LLMs-ICLR-24
- 0
- Awesome-Multimodal-Large-Language-Models
- 111
Language
- Awesome-LLMs-ICLR-24
- -
- Awesome-Multimodal-Large-Language-Models
- -
Adopt for
- Awesome-LLMs-ICLR-24
- 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
- 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
- Awesome-LLMs-ICLR-24
- -
- Awesome-Multimodal-Large-Language-Models
- -
Runtime
- Awesome-LLMs-ICLR-24
- -
- Awesome-Multimodal-Large-Language-Models
- -
License
- Awesome-LLMs-ICLR-24
- MIT
- Awesome-Multimodal-Large-Language-Models
- -
Last pushed
- Awesome-LLMs-ICLR-24
- Apr 4, 2024
- Awesome-Multimodal-Large-Language-Models
- Aug 14, 2026
Categories
- Awesome-LLMs-ICLR-24
- Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
- Awesome-Multimodal-Large-Language-Models
- Evaluation & Observability, LLM Frameworks
Trust and health
Maintenance
- Awesome-LLMs-ICLR-24
- Dormant (18%)
- Awesome-Multimodal-Large-Language-Models
- Very active (96%)
Days since push
- Awesome-LLMs-ICLR-24
- 856d
- Awesome-Multimodal-Large-Language-Models
- 2d
Open issues (now)
- Awesome-LLMs-ICLR-24
- 0
- Awesome-Multimodal-Large-Language-Models
- 111
Stars delta
- Awesome-LLMs-ICLR-24
- Unknown
- Awesome-Multimodal-Large-Language-Models
- +29 (30d)
Open issues delta
- Awesome-LLMs-ICLR-24
- Unknown
- Awesome-Multimodal-Large-Language-Models
- +4 (30d)
Full report
- Awesome-LLMs-ICLR-24
- Trust report
- Awesome-Multimodal-Large-Language-Models
- Trust report
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.
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.
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-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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (azminewasi/Awesome-LLMs-ICLR-24) · observed Aug 8, 2026
- GitHub forks (azminewasi/Awesome-LLMs-ICLR-24) · observed Aug 8, 2026
- Last push (azminewasi/Awesome-LLMs-ICLR-24) · observed Apr 4, 2024
- License file (MIT) · observed Aug 8, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (BradyFU/Awesome-Multimodal-Large-Language-Models) · observed Aug 17, 2026
- GitHub forks (BradyFU/Awesome-Multimodal-Large-Language-Models) · observed Aug 17, 2026
- Last push (BradyFU/Awesome-Multimodal-Large-Language-Models) · observed Aug 14, 2026
- License file (unknown) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-LLMs-ICLR-24 72 · Awesome-Multimodal-Large-Language-Models 18k (synced Aug 8, 2026).
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 and Awesome-Multimodal-Large-Language-Models alternatives (Awesome-LLMs-ICLR-24 markdown twin, Awesome-Multimodal-Large-Language-Models markdown twin), 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 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; Awesome-Multimodal-Large-Language-Models trust report.